Вакансии

Location: Kuala Lumpur, Malaysia About Us Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance. Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution. Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services. About the Role We are at an inflection point where AI is fundamentally reshaping what products can do and how users experience them. We're not just looking for a product manager — we're looking for a hybrid product builder who truly understands AI capabilities and can translate technical potential into real user value. You will lead end-to-end product design for LLM / Agent-powered applications — from zero to one — across domains including intelligent customer service, personalized recommendations, AI assistants, and automated workflows. You are the critical connector between user insights, AI technical capabilities, and business objectives: someone who can discuss RAG retrieval strategies and multi-agent orchestration with ML engineers, while clearly articulating product value and delivery roadmaps to business stakeholders. If you have genuine passion for AI-native products and the ability to define clear product direction amid ambiguity and rapid change, we'd love to build the next generation of AI applications together. Key Responsibilities End-to-End AI Product Lifecycle Management Own the full product lifecycle of AI applications — including intelligent chatbots, AI assistants, recommendation systems, and automated workflows — from requirements definition and product design through to launch and delivery. Produce high-quality PRDs, interaction flow diagrams, and acceptance criteria while driving cross-functional teams to execute efficiently. LLM / Agent Capability Productization Deeply engage in Agent product design, including task planning workflows, tool-calling strategies (Function Calling / MCP), context management mechanisms, multi-agent orchestration logic, and graceful failure/fallback handling. Translate underlying model capabilities into meaningful, perceivable user value. AI Evaluation Framework & Quality Standards Build Harness evaluation frameworks for AI products — designing test scenario sets, evaluation metric systems (accuracy, hallucination rate, task completion rate, etc.), and regression test case libraries. Independently assess AI capability boundaries and drive continuous model iteration and product quality improvement. Conversational Product Design & Knowledge Engineering Lead platform architecture planning for intelligent customer service and conversational assistant products, covering knowledge base structure design, dialogue flow and SOP design, corpus annotation and training strategy, intelligent reply generation, and case summarization. Leverage large-scale data analysis to continuously optimize conversation quality and issue resolution rates. Personalization Engines & Generative UI Products Collaborate deeply with ML and data teams to design recommendation strategies that deliver truly personalized, individualized user experiences. Define generative UI product requirements and drive growth in core business metrics including CTR, conversion rate, and user retention. Establish comprehensive attribution analysis frameworks for recommendation effectiveness. AI Capability Integration with Business Scenarios Develop a thorough understanding of business goals and user pain points. Proactively identify opportunities to apply AI technologies — including RAG, Prompt Engineering, and multimodal capabilities — to concrete use cases. Define clear product evolution roadmaps that balance technical feasibility, user experience, and commercial value. Cross-Functional Collaboration & AI-Native Product Culture Act as the product Owner in close collaboration with algorithm, engineering, data, design, operations, and business teams. Maintain a strong sense of ownership in a fast-moving AI-native environment, continuously track industry developments, and rapidly translate emerging AI capabilities into product innovation opportunities. Requirements Product Experience : 3+ years of product management experience, with at least 1 year of direct hands-on experience delivering AI application products, AIGC products, or Agent products. Must have fully shipped at least one AI-related feature module or standalone product. AI Technical Understanding : Deep understanding of the productization of mainstream AI technologies, including but not limited to: LLM Prompt Engineering, RAG (Retrieval-Augmented Generation) architecture, Agent fundamentals (tool calling / task planning / context window management), and conversational system design. Ability to independently evaluate the product feasibility of technical proposals. Evaluation & Data Capabilities : Proven ability to design AI product evaluation frameworks — independently defining evaluation metrics, building test case sets, and tracking product performance through data analysis. Familiarity with A/B experiment design and a strong data-driven decision-making mindset. Documentation & Communication Skills : Ability to write structured, logically rigorous PRDs, technical requirement documents, and flow diagrams. Excellent cross-functional communication skills with the ability to accurately bridge information between technical and business teams, eliminating information gaps. User Insight & Product Thinking : Sharp ability to translate ambiguous user needs into clearly defined product requirements. Skilled at finding the right product balance between AI capability boundaries and user expectations — avoiding both overpromising and underutilizing model capabilities. Learning Agility & Ownership Mindset : Sustained enthusiasm and capacity for continuous learning in a rapidly evolving AI landscape. Strong sense of ownership — able to proactively define problems, drive decisions, and take accountability for outcomes in highly ambiguous environments. Nice to Have Familiarity with cutting-edge AI engineering practices including MCP ( Model Context Protocol ), Function Calling, and multi-agent orchestration frameworks (e.g., LangChain / AutoGen / CrewAI), enabling deep technical dialogue with engineering teams. Experience with AI Harness / Eval / Benchmark platform product design or usage, with involvement in establishing and maintaining LLM evaluation systems. Background in Fintech / e-commerce / SaaS industries, with an understanding of the unique product design requirements for AI in high-concurrency, compliance-sensitive environments. Bilingual fluency in Chinese and English (spoken and written), with the ability to independently drive product collaboration and cross-cultural communication within international teams. Why Join Us At Bybit, we are committed to fostering a supportive and enriching work environment. Our benefits include: - Study Growth Fund: We support your professional development and continuous learning. - Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation. - Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world. - Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company. - Internal Mobility: Grow with us- Your long-term development is important to us. We offer internal job opportunities to help build your career path.

Location: Москва Команда юридических ИИ-систем и автоматизации правового анализа. Основной стек: Python 3.10+, FastAPI, LangChain / LangGraph, OpenSearch, Redis, Kafka Обязанности * разработка и поддержка промышленных пайплайнов вокруг больших языковых моделей (LLM) — интеграция агентов, RAG-модулей, кэширования и асинхронных очередей * реализация многоагентных систем на базе LangGraph / LangChain — orchestration, routing, memory-модули, логика принятия решений * интеграция LLM API (GigaChat, OpenAI, Gemini, Qwen) и разработка внутренних сервисов общения между агентами * подключение векторных и поисковых движков (OpenSearch, Qdrant) для семантического поиска в юридических документах * разработка внутренних REST/gRPC-API для взаимодействия с фронтом, хранилищем и внешними LegalTech-системами * настройка пайплайнов логирования, трейсинга и мониторинга (Prometheus, Grafana, Loki) * совместная работа с Data Science-командой — внедрение моделей, дообучение и эксперименты в продакшне. Требования * опыт коммерческой разработки на Python 3+ от 3 лет * глубокое понимание асинхронного программирования, многопоточности и очередей сообщений * опыт построения микросервисной архитектуры и REST/gRPC-API * навыки работы с векторными базами данных или поисковыми движками * базовое понимание принципов работы LLM, RAG, агентов и промтинг-пайплайнов. Будет плюсом: * опыт с GraphRAG / LangGraph * знание DevOps-практик (CI/CD, контейнеризация, мониторинг) * опыт интеграции с LegalTech-системами, ML-API или хранилищами нормативных актов * навыки работы с генеративными AI-моделями * опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия * офисный формат работы, после испытательного гибрид обсуждаем * ежегодный пересмотр зарплаты, квартальная и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Задачи: — Работа с NLP и AI-проектами, запуск AI-проектов в продакшн и построение архитектуры вокруг сторонних LLM-решений. — Обучение нейросетевых моделей на GPU-кластере. Требования: — Большой опыт разработки на Python с использованием Pandas, NumPy, Scikit-learn. — Опыт работы с LangChain, vLLM. — Опыт работы с PyTorch, Hugging Face Transformers. — Глубокое понимание машинного обучения, глубокого обучения и NLP. — Опыт запуска AI-проектов в продакшн и построения архитектуры вокруг сторонних LLM-решений. — Опыт обучения нейросетевых моделей на GPU-кластере. — Знание SQL. — Владение Git. — Высшее техническое образование в области Computer Science, прикладной математики, AI/ML или смежных областях. Условия: — Работа в команде экспертов. — Широкий технологический стек, сотни проектов. — Гибкий график работы. — Льготы и меры господдержки, предусмотренные для IT-компаний (аккредитованы Минцифры). — Возможность разрабатывать IT-решения для разных отраслей и пробовать новые технологии. — Возможность работать удалённо с доступом к просторным офисам с зонами коворкинга. — Возможность профессионального роста: стать тимлидом, архитектором, full-stack разработчиком. — Развитая система наставничества, сертификация за счёт компании, участие в конференциях, изучение английского языка. — Активный обмен опытом, митапы, хакатоны, доклады по развитию hard и soft skills. — 50+ клубов по интересам и профессиональных сообществ. — Уютный мерч. — Полный соцпакет и официальная "белая" зарплата. — Корпоративная база знаний и социальная сеть для сотрудников. — Гибкий ДМС с возможностью подключения родственников.

Location: Bengaluru, India We are looking for a Senior AI Quality Engineer (L3+) with strong Python skills and hands-on experience testing AI and ML systems. You will analyze requirements, define evaluation strategies, build automated checks, validate models and outputs, test APIs and data pipelines, verify defects through resolution, and contribute to release readiness for your assigned product area. You will both evaluate AI systems and design, build, and operate QE agents for the software testing life cycle. AI-assisted work is part of everyday QE practice on this engagement, with Ragas, DeepEval, promptfoo, Cursor, and other AI and ML tools used for evaluation, scripting, test generation, analysis, and maintenance. You will also build golden datasets, calibrate LLM-as-judge methods, test agentic workflows and RAG systems, implement self-healing and intelligent test approaches, and diagnose quality, safety, performance, and cost failures across distributed AI services. This position is intended for Hyderabad-based candidates who are available to work from the office. The client delivers AI agents, retrieval systems, models, and data pipelines across a broad technology landscape. Quality is part of delivery through evaluation gates, automated regression, model and data validation, security and privacy checks, and evidence that a release is ready. Python is the primary language for AI evaluation, model validation, API testing, data-pipeline quality, and QE agent design. The role combines deterministic automation with probabilistic evaluation to assess AI behavior across models, data, retrieval, tools, and integration surfaces, and designs and operates agents that support the software testing life cycle. Analyze requirements and define evaluation strategies, golden datasets, regression benchmarks, baselines, thresholds, and blocking metrics with domain-expert review. Design, build, and orchestrate AI agents, persona-based workflows, and reusable skills for the STLC using LangChain, LangGraph, or Semantic Kernel. Build automated evaluations and benchmarks for LLMs, agents, RAG, retrieval, tool trajectories, and contracts using Ragas, DeepEval, and promptfoo. Calibrate LLM-as-judge against human grading, manage non-determinism through repeated runs, and detect regression and drift. Write and maintain Python and pytest automation for AI, ML, API, data, and integration workflows. Use Cursor and agentic tools to analyze requirements, generate and review tests, and maintain evaluation suites. Validate deep-learning, reinforcement-learning, and predictive models using LangChain. Build AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, and defect triage, plus intelligent test selection and defect prediction. Integrate LLMs, APIs, embeddings, vector databases, retrieval pipelines, and enterprise data sources into QE frameworks using Python. Test REST and streaming APIs, agent tools, and RAG and MCP integrations, including authentication, errors, retries, and recovery. Test data and retrieval pipelines for schema integrity, lineage, idempotency, fallback, promotion gates, and rollback. Integrate evaluations, benchmarks, and agent workflows into QE frameworks and CI/CD on GitHub Actions, review Allure results, and keep TestRail aligned with automated coverage. Test prompt injection, jailbreaks, unsafe tool use, data exfiltration, access control, PII controls, and guardrails. Measure accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnose distributed failures. Implement tracing, monitoring, and production evaluation using Langfuse, LangSmith, or a comparable AI observability platform. Deploy and operate QE agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Deploy and validate AI and ML solutions on AWS or Azure, including secrets, database services, and cloud-native test infrastructure. Report, track, and verify defects in Jira, and work with domain experts, product, and engineering on traceable coverage and untested scope. Contribute to release readiness for the assigned product area without owning sign-off for the whole team. Strong Python proficiency with pytest for test and evaluation automation. Hands-on experience building and orchestrating AI agents and agentic workflows with LangChain, LangGraph, Semantic Kernel, or comparable frameworks. Hands-on experience building AI evaluations and benchmarks with Ragas, DeepEval, promptfoo, or equivalent tools. Hands-on use of Cursor or agentic development tools for test generation and maintenance. Experience defining AI evaluation strategies, golden datasets, regression benchmarks, and LLM-as-judge calibration, including non-determinism and drift. Experience validating deep-learning, reinforcement-learning, and predictive models with LangChain. Experience building AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, defect triage, intelligent test selection, and defect prediction. Experience integrating LLMs, APIs, embeddings, vector databases, and retrieval pipelines with automation frameworks in Python. Experience testing REST and streaming APIs, agent tools, RAG workflows, and MCP integrations. Experience testing data and retrieval pipelines for correctness, lineage, and safe promotion. Experience integrating AI evaluation and agent workflows with automation frameworks and CI/CD using GitHub Actions, Allure, TestRail, or equivalent tools. Experience testing AI security, privacy, access control, and guardrails. Experience measuring accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnosing distributed failures. Experience with AI tracing, monitoring, and production evaluation using Langfuse, LangSmith, or comparable tools. Familiarity with AWS, Azure, or GCP cloud AI services and agent platforms for deploying and validating AI and ML solutions. Experience deploying and operating agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Experience managing defects through resolution in Jira or an equivalent tool. Ability to work with domain experts, product, and engineering on ground truth and coverage gaps. Experience contributing to release readiness for an assigned product area. Playwright and TypeScript experience for UI and API automation. Real-device and cross-browser execution using LambdaTest, Sauce Labs, or an equivalent platform. Knowledge of AI-driven test optimization and coverage analytics using SeaLights or an equivalent platform. Experience contributing reusable components to shared automation frameworks and reviewing automation code through Git and GitHub workflows. Experience managing test data and troubleshooting test environments needed for reliable execution. Experience triaging security findings and validating operational readiness, monitoring, rollback, and recovery controls. Experience producing audit-ready evaluation reports and improving shared evaluation patterns, automation libraries, and datasets.

Requirements: — 4–5+ years of AI/ML or Data Science experience — Advanced Python, SQL, and ML experience — Hands-on experience with LLMs, RAG, embeddings, and transformers — Experience with PyTorch/TensorFlow, Hugging Face, LangChain or LlamaIndex — Experience with vector databases such as Pinecone, FAISS, Milvus, or OpenSearch — MLOps experience with Docker, Kubernetes, MLflow, and CI/CD — AWS and production AI/ML deployment experience The role involves building production GenAI systems, RAG pipelines, recommendation/personalization models, and scalable ML infrastructure from development through deployment. The position is fully remote, with full-time or contract options.

Проект: Мы разрабатываем и поддерживаем продукт ERA - систему комплексной диагностики продуктов и услуг. Наше решение позволяет производить диагностику, анализ и аудит продукта для получения комплексного решения развития, снятия барьеров роста. Цель проекта — построить полностью автономную экспертную систему, где ключевую роль играют смарт-агенты, выполняющие комплексный анализ без участия человека. Технологический стек: Язык программирования: Python 3.12+ Фреймворки: FastAPI, LangChain / LangGraph, FastStream Базы данных: PostgreSQL, Redis Очереди: Kafka, RabbitMQ Поиск и аналитика: Opensearch, Elasticsearch CI/CD: Jenkins, Nexus, BitBucket, SonarQube Инфраструктура: OpenShift (Kubernetes), DropApp, Docker Управление: Jira, Confluence Требования: Высшее профильное образование Опыт коммерческой разработки на Python от 4 лет (по ТК РФ) Уверенное знание Python 3.7+ (в проекте используется 3.12+) Понимание и успешный опыт применения паттернов проектирования (GoF, SOLID, GRASP) Опыт проведения код-ревью и работы по GitFlow Опыт разработки агентных систем с помощью langchain, langgraph и успешное внедрение в промышленную среду Опыт разработки приложений, основанных на микросервисной архитектуре Уверенное знание SQL и понимание принципов работы реляционных СУБД (PostgreSQL) Опыт написания unit-тестов Опыт разработки асинхронных приложений на Python Понимание жизненного цикла разработки приложений в контексте Agile Важно: Опыт и желание работать в команде, умение аргументировать свои решения и договариваться Задачи: Разработка co-pilot решения на базе данных RAG и применении LLM Разработка и интеграция AI-агентов, исследование новых поверхностей взаимодействия с AI Разработка микросервисов на Python в качестве MCP инструментов Анализ требований, формализация функционала, проектирование архитектуры взаимодействия агентов и сервисов Участие в планировании, декомпозиции задач и оценке сложностей, планировании спринтов на основе SCRUM церемоний Проведение код-ревью, поддержание высокого стандарта качества и чистоты кода Тесное взаимодействие с командой на всех этапах разработки Участие в подготовке и выпуске релизов, взаимодействие со смежными подразделениями Документирование своей работы и менторство джуниор-разработчиков Условия: Возможность участия в интересном проекте Возможность профессионального и карьерного роста в компании Опыт работы в команде профессионалов Уровень заработной платы обсуждается индивидуально Формат работы - офисный формат в Москве на период ИС, далее возможен гибрид

Проект: Мы разрабатываем систему, автоматизирующую весь жизненный цикл инвестиционной сделки. Наша инвестиционная платформа — уникальное решение на российском рынке для выдачи и сопровождения инвестиционного финансирования юридическим лицам. Современный стек и архитектура: - Микросервисная архитектура: Kubernetes, Docker, Istio, Kafka - Микрофронтовая архитектура: модульные фронтенд-модули - Интеллектуальные агенты: AI-банкир, AI-методолог - Производственные агенты: разработчик, тестировщик, ревьювер - Системы автоматизации: CI/CD, логистика, мониторинг Требования: - Глубокое знание JavaScript/TypeScript, React, Redux - Знание парадигм программирования, CSS, HTML - Опыт разработки микрофронтов - Fullstack-опыт: backend (микросервисы) + frontend - Опыт работы с автоматизациями и системами CI/CD - Агентный режим разработки: работа с LLM (LangChain, LangFuse, OpenCode, Codex, Claude, инференсы) - Опыт опенсорс-контрибуции и патчинга внешних библиотек - Умение проектировать и внедрять исследовательские гипотезы в продакшн Задачи: - Разработка и внедрение AI-агентов (AI-банкир, AI-методолог) - Разработка производственных агентов (разработчик, тестировщик, ревьювер) - Разработка и внедрение систем автоматизации производственного процесса - Исследования и выдвижение гипотез, проверка их в пилотных проектах - Интеграция решений в инвестиционный ландшафт Сбера - Реализация ключевых frontend-решений с учетом микрофронтовой архитектуры - Проведение code review и техническое проектирование - Наставничество и передача знаний команде Условия: - Возможность участия в интересном проекте - Возможность профессионального и карьерного роста в компании - Опыт работы в команде профессионалов - Уровень заработной платы обсуждается индивидуально - Формат работы - удаленно на территории РФ

Location: New York, NY, United States About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. Job Summary Fanatics Betting & Gaming (FBG) is building the industry's most trusted, scalable, and fan-centric sportsbook experience. Our Internal Platforms power critical systems that support customer operations, marketing, support, risk, and data—through a combination of Java backend services, APIs, event-driven systems, and integrations with the SaaS and CRM platforms our business teams run on. We are seeking a Staff Java Software Engineer to set technical direction for the services and integrations that Internal Platforms runs on. This is a senior individual contributor role for an engineer who operates with minimal guidance, takes on our most complex and often cross-functional problems, and raises the bar for everyone around them. At this level, your impact is measured beyond the code you write. You will own architecture for significant parts of the platform, drive decisions that span multiple teams and systems, mentor other engineers, and partner directly with Product, Operations, Support, Marketing, Risk, and Data leadership to shape what gets built and why. You will still be hands-on—Staff engineers at FBG write and review code every week—but you will spend meaningful time on design, technical strategy, and multiplying the output of the teams you work with. You do not need to be a Salesforce specialist. Some of our systems integrate with Salesforce for CRM and workflow automation, so exposure to it is a nice to have—but we're hiring for strength in Java, distributed systems, and integration design, and we expect you to pick up the platform specifics on the job. Key Responsibilities Technical Leadership & Architecture (Primary Focus) Own the architecture for major services, integrations, and data flows within Internal Platforms, from problem definition through production operation. Lead design for complex, often cross-functional initiatives—defining the approach, surfacing trade-offs, and driving alignment across engineering teams and stakeholders. Break ambiguous, department-level problems into clear technical plans and workstreams that other engineers can execute against. Evaluate and select technologies, patterns, and vendors; know when a sophisticated design approach is warranted and when the simpler answer is the right one. Establish and evolve engineering standards for the team—API design, testing strategy, observability, deployment, and integration patterns—and get them adopted in practice, not just documented. Identify systemic technical debt and reliability risk across the platform, build the case for addressing it, and sequence the work alongside feature delivery. Hands-On Engineering Design, build, and maintain Java-based backend services (Spring Boot or similar frameworks) that are scalable, resilient, and well-tested. Take on the hardest technical problems on the team: high-throughput event processing, complex multi-system integrations, thorny data integrity issues, and production incidents that cross service boundaries. Diagnose problems at the root cause rather than the symptom, and translate what you learn into durable fixes and better instrumentation. Set the quality bar through your own code and through high-signal code reviews that teach as well as gate. Contribute to internal web applications and user interfaces backed by Java services where it moves the work forward. Mentorship & Force Multiplication Mentor and coach Senior and mid-level engineers through design review, pairing, and direct feedback; help them grow the judgment to handle complexity independently. Create learning and growth opportunities for teammates—delegate meaningful design work, sponsor stretch assignments, and share context generously. Onboard and ramp new engineers into the platform's systems and conventions. Write the documentation, runbooks, and design records that let the team operate and extend the platform without you in the room. Model an inclusive, collaborative environment where opposing views are actively invited and disagreement is resolved on the technical merits. Cross-Functional Partnership & Delivery Partner with Product Managers, Technical Program Managers, and business stakeholders to shape roadmaps—bringing technical feasibility, sequencing, and risk into the conversation early rather than reacting to finished requirements. Work directly with Marketing, Operations, Support, Risk, Compliance, and Data teams to ensure platform capabilities meet business needs and regulatory obligations across jurisdictions. Represent Internal Platforms in architecture and design discussions with other engineering teams, negotiating clean contracts and shared ownership boundaries. Communicate complex technical problems, trade-offs, and decisions clearly to both engineering and non-engineering audiences, including leadership. Drive predictable delivery for initiatives you own: scope, estimate, unblock, and own the outcome—including the parts that depend on other teams. Engage external partners and vendors on integration design and technical escalations where needed. Third-Party & SaaS Platform Integration Own the integration architecture between our Java services and the third-party platforms our business teams depend on—REST APIs, platform events, middleware—including data ownership, consistency, and failure handling. Define scalable, reusable integration patterns and prevent the platform sprawl that comes from point-to-point fixes. Lead resolution of complex integration issues spanning backend services, vendor platforms, and data pipelines. Partner with platform-specialist engineers (including our Salesforce-focused engineers) to keep integration boundaries clean and maintainable. Operational Excellence Own production health for the services in your remit: SLOs, alerting, on-call quality, incident response, and post-incident follow-through. Anticipate failure modes, capacity limits, and blockers before they surface, and build the safeguards in advance. Improve engineering practice across the team—CI/CD, test automation, release safety, and developer productivity. Hold the line on the standards required of a regulated, highly available gaming platform. Qualifications Required 8+ years of professional software engineering experience, with deep expertise in Java. Demonstrated ownership of the architecture and delivery of complex production systems, including work that spanned multiple teams or functions. Strong experience designing APIs and integrating distributed systems, with sound judgment about consistency, failure handling, and service boundaries. Track record of operating with minimal guidance: taking an ambiguous problem, defining the approach, and driving it to production. Experience mentoring engineers and raising technical standards on a team—through design review, code review, and direct coaching. Ability to communicate complex technical concepts and trade-offs clearly in writing and in person, to technical and non-technical audiences alike. Experience with Git-based workflows, code reviews, and CI/CD pipelines. Experience operating services in production, including on-call ownership and incident response. Preferred Experience with event-driven architectures (Kafka or similar) at meaningful scale. Experience in cloud environments (AWS preferred), including infrastructure-as-code. Experience with highly available or regulated systems (gaming, fintech, payments). Experience contributing to internal web UIs or tooling, including Java-backed web frameworks and/or modern front-end frameworks consuming Java APIs (React, Angular, etc.) Experience working with AI model or agent platforms, including building on or integrating agents into production workflows. Familiarity with platforms such as Anthropic, OpenAI, Amazon Bedrock, LangChain, Google Vertex AI, Microsoft Copilot Studio, CrewAI, or AutoGen is a plus. Fluency with AI-powered coding assistants to design, build, and test new functionality from the ground up—moving quickly while maintaining high code quality and test coverage—and interest in helping the broader team adopt these workflows effectively. Nice to Have Exposure to Salesforce as an integration target or platform—Salesforce APIs, platform events, or light development work in Apex, Lightning Web Components, or Flows. Helpful context, not a requirement. Salesforce certifications (Administrator, Developer). What We Value Passion for building high-quality internal platforms that directly impact business outcomes. Strong ownership mindset: you own the outcome, not just your piece of it. A solutions-first approach—you anticipate problems and bring options, not just escalations. Genuine interest in making other engineers better. Comfort operating in a fast-paced, evolving environment with incomplete information. Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/ Salary Range $1 — $1 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Location: New York, NY, United States About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. Job Summary Fanatics Betting & Gaming (FBG) is building the industry’s most trusted, scalable, and fan-centric sportsbook experience. Our Internal Platforms power critical systems that support customer operations, marketing, support, risk, and data—through a combination of Java backend services, APIs, event-driven systems, and Salesforce platform capabilities. We are seeking a Senior Java Software Engineer to build and maintain scalable services that power our internal platforms. This role is full stack focused, with opportunities to contribute to user-facing internal tools as needed. Experience with Salesforce development and integrations is a strong plus, as many of our systems interface directly with Salesforce for CRM, workflow automation, and customer operations. This is a hands-on engineering role for someone who enjoys owning features end-to-end, solving complex integration problems, and working closely with cross-functional partners. Key Responsibilities Full Stack Engineering (Primary Focus) Design, build, and maintain Java-based backend services (Spring Boot or similar frameworks). Develop scalable, reliable, and well-tested services that integrate with Salesforce and other internal/external systems. Participate in architecture and design discussions for APIs, data flows, and distributed systems. Write high-quality, maintainable code with strong unit and integration test coverage. Troubleshoot and resolve complex issues across services, integrations, and data pipelines. Contribute to internal web applications and user interfaces backed by Java services. Internal UI & Application Development (Nice to Have / Secondary) Collaborate with frontend or full-stack engineers to support internal users across Operations, Support, Marketing, Risk, and Data teams. Partner with Product and UX (where applicable) to translate workflows into intuitive internal tools. Collaborate with backend engineers to define clean API contracts between UI layers and services. UI technologies may include Java-backed web apps, REST-driven front ends, or modern JavaScript frameworks consuming Java APIs. Note: This role does not require deep UI specialization, but comfort collaborating across UI and backend boundaries is valued. Salesforce & CRM Platform Integration (Secondary Focus) Build and maintain integrations between Java services and Salesforce using REST APIs, events, or middleware. Contribute to Salesforce development when needed, including Apex, Lightning Web Components, Flows, and configuration. Partner with Salesforce-focused engineers to ensure clean integration patterns, data integrity, and platform scalability. Help troubleshoot Salesforce-related issues tied to backend services, UI workflows, or integrations. Collaboration & Delivery Work closely with Product Managers, Technical Program Managers, and cross-functional partners to translate requirements into technical solutions. Collaborate with Marketing, Operations, Support, Risk, and Data teams to ensure systems meet business and regulatory needs. Participate in sprint planning, estimation, and delivery, ensuring predictable and high-quality outcomes. Communicate technical decisions, trade-offs, and implementation details clearly to stakeholders. Operational Excellence & Best Practices Follow and help improve engineering best practices including CI/CD, code reviews, testing strategies, and observability. Ensure services are performant, observable, and resilient, with appropriate logging, metrics, and alerting. Contribute to documentation, runbooks, and long-term platform improvements. Identify technical debt and propose pragmatic solutions. Qualifications Required 6+ years of professional software engineering experience with Java. Strong experience building backend services in a production environment. Experience developing APIs and integrating services with other systems. Familiarity with distributed systems, data flows, and service-to-service communication. Experience with Git-based workflows, code reviews, and CI/CD pipelines. Strong problem-solving skills and ability to work independently on complex systems. Excellent communication and collaboration skills. Preferred Experience contributing to internal web UIs or tooling, including: Java-baced web frameworks, and/or Modern front-end frameworks consuming Java APIs (React, Angular, etc.) Experience working with Salesforce as an integration partner or platform: Apex, Lightning Web Components, Flows, or Salesforce APIs Experience integrating Salesforce with Java-based services. Experience with: Event-driven architectures (Kafka or similar) Cloud environments (AWS preferred) Highly available or regulated systems (gaming, fintech, payments) Experience working with AI model or agent platforms, including building on or integrating agents into production workflows. Familiarity with platforms such as Anthropic, OpenAI, Amazon Bedrock, LangChain, Google Vertex AI, Microsoft Copilot Studio, CrewAI, or AutoGen is a plus. Familiarity using AI-powered coding assistants to design, build, and test new functionality from the ground up, with the ability to move quickly while maintaining high code quality and test coverage. Interest in leveraging emerging tools and workflows to improve developer productivity and platform throughput. Salesforce certifications (Administrator, Developer) are a plus, but not required. What We Value Passion for building high-quality internal platforms that directly impact business outcomes. Strong ownership mindset and commitment to engineering excellence. Collaborative approach and desire to continuously learn and improve. Comfort operating in a fast-paced, evolving environment. Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process. For information about our benefits, please visit https://benefitsatfanatics.com/ Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/ Salary Range $1 — $1 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Location: New York, NY, United States About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. About the Team Launched in 2021, Fanatics Betting and Gaming is the online and retail sports betting subsidiary of Fanatics, a global digital sports platform. The Fanatics Sportsbook is available to 95% of the addressable online sports bettor market in the U.S. Fanatics Casino is currently available online in Michigan, New Jersey, Pennsylvania and West Virginia. Fanatics Betting and Gaming operates twenty-two retail sports betting locations, including the only sportsbook inside an NFL stadium at Northwest Stadium. Fanatics Betting and Gaming is headquartered in New York with offices in Denver, Leeds and Dublin. Fanatics Betting & Gaming (FBG) is building the industry’s most trusted, scalable, and fan-centric sportsbook experience. Our Internal Platforms team powers the systems that enable customer operations, marketing, support, and data-driven decision making—centered around Salesforce and deeply integrated backend services. We are seeking a Senior Salesforce Engineer to develop, and maintain scalable Salesforce solutions that support critical internal workflows and customer-facing operations. This is a hands-on engineering role requiring strong Salesforce development expertise, comfort working across integrations and distributed systems, and a passion for building high-quality internal platforms. You will work closely with Marketing, Operations, Product, Data Engineering, and Backend Engineering teams to deliver robust Salesforce capabilities that integrate with third-party systems and internal APIs. Responsibilities Salesforce Engineering & Platform Development Build, and maintain custom Salesforce solutions using Apex, Lightning Web Components (LWC), Flows, and declarative features . Participate in the end-to-end implementation of Salesforce enhancements, from requirements gathering through deployment and support. Apply scalable and maintainable patterns for triggers, flows, automation, and custom components. Write high-quality, testable code with appropriate unit test coverage and participate in code reviews. Integrations & Distributed Systems Develop and maintain integrations between Salesforce and external/internal systems using REST/SOAP APIs, middleware, and event-driven patterns. Partner with backend engineers to design clean integration contracts between Salesforce and Java-based services. Troubleshoot and resolve complex integration and data flow issues across systems. Collaboration & Stakeholder Partnership Collaborate with Product Managers, Business Analysts, and cross-functional partners to translate business requirements into technical solutions. Partner with Marketing, VIP, Operations Support, Risk, and Data teams to ensure Salesforce solutions meet business and regulatory needs. Clearly communicate technical decisions, trade-offs, and implementation details to both technical and non-technical stakeholders. Data Management & Platform Quality Support data migrations, imports/exports, and ongoing data quality initiatives. Ensure strong data integrity, security, access control, and performance across the Salesforce ecosystem. Contribute to platform reliability, availability, and operational excellence. Documentation & Best Practices Create and maintain technical documentation for Salesforce customizations, configurations, and integrations. Follow and help evolve engineering best practices related to Salesforce DX, Git workflows, CI/CD pipelines, testing, and deployment strategies . Continuously identify opportunities to reduce technical debt and improve platform scalability. Experience and Skills Required 3-5 years of hands-on Salesforce engineering experience , with strong proficiency in Apex, Lightning Web Components, and Salesforce automation. Strong experience with Salesforce configuration, including custom objects, flows, validation rules, and security model design . Experience building and maintaining API-based integrations between Salesforce and third-party or internal systems. Working knowledge of Salesforce DX, Git, and CI/CD pipelines . Ability to work independently on complex projects while collaborating effectively within a team. Strong problem-solving skills and attention to detail. Preferred Salesforce Platform Developer I or II certification. Additional Salesforce certifications (Administrator, App Builder, Architect). Experience with: Data migrations and large-scale data operations Event-driven architectures and distributed systems Highly available, production-critical platforms Experience working closely with cross-functional teams such as Marketing, Product, Operations, and Data. Experience working with AI model or agent platforms, including building on or integrating agents into production workflows. Familiarity with platforms such as Anthropic, OpenAI, Amazon Bedrock, LangChain, Google Vertex AI, Microsoft Copilot Studio, CrewAI, or AutoGen is a plus. Familiarity using AI-powered coding assistants to design, build, and test new functionality from the ground up, with the ability to move quickly while maintaining high code quality and test coverage. Interest in leveraging emerging tools and workflows to improve developer productivity and platform throughput. What We Value Passion for building high-quality internal platforms that directly impact business outcomes. Strong ownership mindset and commitment to engineering excellence. Collaborative approach and desire to continuously learn and improve. Comfort operating in a fast-paced, evolving environment. Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process. For information about our benefits, please visit https://benefitsatfanatics.com/ Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/ Salary Range $128,000 — $160,000 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Задачи: — Проектировать и разрабатывать AI-агентов; — Разрабатывать и оптимизировать промпты, учить агентов взаимодействовать с продуктами компании; — Решать задачи NLP: классификация, суммаризация, NER, семантический поиск, кластеризация и другие; — Писать поддерживаемый, читаемый код в рамках разработки AI-агентов (Python 3.12+); — Участвовать в разработке на всех этапах жизненного цикла AI/ML (от предобработки данных до деплоя и мониторинга в проде); — Выводить GenAI-приложения в прод совместно с DevOps-командой. Требования: — Опыт создания агентов на современных фреймворках (LangChain, LangGraph, LlamaIndex, Agno, Pydantic AI Harness и др.); — Знание стратегий планирования AI-агентов (ReAct, Plan-and-Execute и т.д); — Уверенная работа с LLM API (OpenAI, vLLM, Anthropic и др.); — Понимание агентских протоколов (MCP, A2A, AG-UI); — Сильный prompt engineering + chain/tool/function calling; — Опыт работы с векторными БД (Weaviate, Pinecone, Qdrant, Milvus и др.); — Опыт оценки качества LLM-систем (RAGAS, DeepEval, LLM-as-a-Judge или аналоги); — Опыт работы с Linux, Git, Docker, умение упаковать своего агента в контейнер. Условия: — Официальное оформление по ТК РФ; — Профессиональные митапы и встречи для обмена опытом; — Сообщества по интересам: спорт, игры, книги, аниме; — Гибкий график без контроля времени работы; — ДМС со стоматологией с первого месяца; — Обучение: сертификация, профильные курсы, конференции, митапы, хакатоны, CTF-ы; — Свободная атмосфера без дресс-кода и бюрократии; — Поддержка креатива и возможность профессионального роста; — Скидки на фитнес, покупки и многое другое; — Корпоративные мероприятия, спортивные старты, мерч и подарки; — Команда экспертов, выступления на конференциях, публикации; — Забота о здоровье и регулярные синки по проектам; — Прозрачные коммуникации и свобода от бюрократии; — Финансирование сертификации, обучения и участия в мероприятиях.

Задачи: — разработка классических LLM агентов — разработка tools для агентов, а также skills-инструкций для LLM — воспроизведение основных паттернов разработки в продуктовой парадигме — участие в обсуждении требований с бизнес-заказчиками — контроль качества agent-pipeline — декомпозиция верхнеуровневых задач на исполнимые в рамках разработки — вывод в production llm-приложений, agent'ских систем. Требования: — знания в области: математических анализ, линейная алгебра, Deep Learning, Classic ML, Python (async, fastapi, Semaphore) — высшее образование (МГУ, Физтех, ВШЭ, РЭШ и тд) — понимание построенных целевых решений, предложения по внесению изменений в архитектуру — опыт работы в open-source LLM (GLM5.2, qwen 3.5, etc) — знание фреймворков LangChain, LangGraph, fastapi, huggingface, а также regex и иных библиотек по обработке текста — vanilla RAG, Advanced RAG, работа с контекстом llm — базовое знание принципов промышленной разработки на python (SOLID, Singleton) — базовый MLops (работа с version control, CI/CD) Будет плюсом: — опыт работы с openclaw, его форками — использование llm GigaChat, Kandinsky — опыт работы в финтехе — опыт использования и доработки OCR. Условия: — комфортный современный офис рядом с м. Кутузовская — возможность выбрать удобный график – офис/гибрид — ежегодный пересмотр зарплаты, квартальная и годовая премия (премия указывается в зависимости от должности и системы премирования) — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) — расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа — гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ — подписка Прайм с возможностью совместного использования на трёх близких — вознаграждение за рекомендацию друзей в команду Сбера.

Location: British Columbia Workplace: Hybrid Safe Software is looking for an experienced developer to join our Cloud Applications team and take a leading technical role in our FME AI Service, AI Assist Plugin, and future AI product offerings. This is deliberately a hybrid role. We need the software engineering depth to build and operate production services, alongside the experimental instinct to determine empirically what makes an LLM-based agentic system work. Many of the problems don't have standard solutions yet, so you'll be the person who designs the experiment, measures the result, and turns the finding into a shipped improvement. We have an immediate opening and are excited to find the right candidate to join our team. ABOUT THE TEAM As a member of our Cloud Applications development team, you will take technical leadership of designing and developing new features, as well as maintaining existing functionalities in our products. You will primarily work in Python (FastAPI and LangChain), with a willingness to learn new languages and technologies as needed. You will also contribute to support our live applications. The team practices agile development and utilizes Kanban boards to manage their backlog, epics and stories. As a Staff Software Developer - AI Products, you will regularly participate in team meetings (stand-ups, planning), lead technical decision-making processes, and work closely with our Product Managers, UX Designers, and QA, as well as Customer Experience team members who review live traces from AI applications. WHAT YOU’LL BE DOING As a Staff AI Developer, you will: - Design and build server-side services and APIs (FastAPI). - Design and build modular agentic workflows to augment the FME platform capabilities. - Stay up to speed with developments in AI and LLM tooling, and incorporate relevant ideas into our products. - Participate in every aspect of the development cycle, including shaping specifications with the product owner, developing clean and maintainable code, writing unit and integration tests, doing code reviews, and maintaining and improving existing code. - Collaborate, support, and share expertise with other team members. - Work closely with the product owner and design team to deliver a high quality and consistent user experience. - Maintain and continuously improve the building and delivery of our AI service to reduce query latency and improve assistant response quality. - Investigate, evaluate the impact, and resolve anomalies and bugs in live applications. - Participate in the team’s light on-call rotation to support live applications. QUALIFICATIONS, SKILLS, AND COMPETENCIES Safers come from a variety of backgrounds with a diversity in skills and knowledge. That said, we find that those who are most successful in this role have experience with the following areas. Background - Bachelor's degree in Computer Science or a related field, or equivalent experience. - Typically, at least 7 years of experience working on production backend systems. - A track record of leading technically ambiguous projects and raising the capability of the engineers around you. Technical skills - Project or work experience with agentic LLM-based applications, including techniques such as RAG, prompt and context engineering, and tool calling. - Demonstrated experience evaluating LLM systems: building eval sets, defining quality metrics, and tracing and debugging non-deterministic behaviour. - Depth in at least one modern backend language, and the ability to pick up others as the work demands. - Experience with a modern backend web framework such as FastAPI. How you Work - An empirical mindset — comfort designing and running experiments to reach a decision, rather than reasoning about which approach should be best. - Strong interest in the craft, a curious mind, and eagerness to learn new tools and technologies. - Able to explain technical tradeoffs clearly to teammates and stakeholders. - Excellent organizational skills, with the judgment to prioritize your own work efficiently. - Resourcefulness and the ability to work well in a team-based environment. DESIRED SKILLS / BONUS POINTS We don't expect any single candidate to have all of these. Evaluation and experimentation: - Familiarity with LLM evaluation libraries such as Ragas, DeepEval, openevals, promptfoo, Inspect, or TruLens. - Experience with LLM observability and tracing tooling such as LangSmith, Langfuse, Braintrust, or Arize Phoenix. - Statistical literacy: A/B testing, significance, and sample sizing for model and prompt comparisons. - A background in machine learning or data science, or experience collaborating closely with those who do. - Experience with the problem domain: FME, other ETL tools, spatial data processing, or data integration. Retrieval and infrastructure: - Working knowledge of embeddings, vector databases (e.g. pgvector), and chunking and retrieval strategy. - Experience with Docker and deploying applications on Kubernetes. - Experience working with services from a cloud provider, such as AWS, GCP, or Azure. - Understanding of common client and server side vulnerabilities (e.g. OWASP Top 10) as well as security vulnerabilities specific to LLM–based applications. WHAT YOU CAN EXPECT When you choose a career with Safe Software, you’re also choosing… - Meaningful Work: Make a profound impact across our business, workplace and data integration product. - A Supportive Environment: Feel empowered to share your ideas and implement them with high autonomy and team support. - Social Responsibility: Become part of a team that finds meaningful ways to give back https://www.safe.com/about/community-initiatives/. Take paid time off to volunteer for one of our Safe-organized opportunities that align with our core community initiatives. - Learning & Career Development: We believe in your continuous learning and growth. Take advantage of an annual learning budget and training programs paid for by Safe. - Flexible Working Hours: Flexible and remote-friendly work arrangements to fit your lifestyle. Work when you want, and how you want to. - Work-Life Balance: There's no place for burnout at Safe. Maintain a healthy balance of your personal and work life without splitting yourself in two. Enjoy 3 weeks of vacation to recharge, plus an additional paid 6 seasonal days off per year. - Health & Wellness: Extended health, dental, health or lifestyle spending, and counseling benefits from day 1. That’s right, no waiting period. - Family is important: We are dedicated to supporting our employees through parenthood and offer a Parental Leave Top-Up Program for new parents through childbirth or adoption. - Shared Success: Share in Safe’s success with our bi-annual profit sharing and RRSP/TFSA matching program. - An Accessible Commute: Regardless of your commute method, we’re located close to public transit, and provide complimentary parking and bike storage for our team! ABOUT SAFE SOFTWARE Safe Software transforms organizations with FME, the only All-Data Any-AI Enterprise Integration Platform connecting all your data, anywhere, at any velocity. With over 30 years of expertise and 25,000+ enterprise customers across 125+ countries, we simplify your data journey, wherever it leads. Founded in 1993, Safe is headquartered in Surrey, BC with over 300 team members and counting. We’re always looking for talented individuals with diverse backgrounds who are determined to learn and grow. Are you ready to join the team? OUR COMMITMENT TO DIVERSITY AND INCLUSION Safe Software is an equal opportunity employer and we truly believe that innovation and strength begin with diversity and inclusion. We welcome all candidates regardless of race, gender identity or expression, sexual orientation, age, ability, disability, national or ethnic origin, political belief, religion, or family status. Should you require accommodations during the recruitment process, please contact hr_dept@safe.com. NOTICE REGARDING USE OF AUTOMATED TOOLS IN HIRING At Safe Software, we use automated tools to help us process applications and identify qualified candidates. These tools may assist in screening resumes and summarizing interview content based on the information you provide. This helps our team review applications efficiently while ensuring every candidate is considered based on the requirements of the role. Please be assured that the final hiring decision is always made by a member of our team. For more information on how we protect your privacy, please see our policy https://www.safe.com/legal/#applicant-privacy-policy. If you have questions about this process, please contact us at hr_dept@safe.com.

ASOS.com — британский онлайн‑ритейлер модной одежды, обуви, аксессуаров и косметики. Размер команды — 1k-5k, инвестиции — £587.5M. Технологии и инструменты: Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Semantic Kernel, LangChain, LangGraph, AutoGen/CrewAI, Python или C#/.NET. Фаундеры компании: Nick Robertson, Andrew Regan, Quentin Griffiths, Deborah Thorpe. В компании работают русскоговорящие сотрудники.

Location: Москва Мы строим мультиагентную систему для раннего выявления рисков в сегменте микро- и малого бизнеса - систему, где AI-агенты анализируют данные, находят сигналы ухудшения и помогают бизнесу принимать решения в режиме, близком к реальному времени. Это не просто аналитика и не просто ML - это новая архитектура работы с рисками: от данных к автономным интерпретациям и рекомендациям. Мы ищем дата-аналитика, который хочет работать на переднем крае: разрабатывать и обучать AI-агентов, собирать сложные пайплайны данных и участвовать в создании системы, которая масштабируется на весь портфель. Обязанности * разработка и внедрение AI-агентов на базе LLM (Python, LangChain, LangGraph) * проектирование и внедрение мультиагентной платформы для обработки больших объемов данных о деятельности субъектов малого и микробизнеса * создание механизмов автоматического мониторинга и выявления ранних признаков риска возникновения просроченной задолженности среди клиентов сегмента ММБ * организация работы команды специалистов по разработке AI-решений: формирование требований, контроль качества выполнения проектов и своевременность исполнения задач * разработка методологии и инструментов автоматизированного когортного анализа клиентского портфеля малого и микробизнеса * подготовка прогнозных моделей, основанных на искусственном интеллекте, позволяющих своевременно реагировать на изменения состояния клиентской базы * взаимодействие с профильными департаментами компании для согласования подходов к обработке данных и интеграции решений. Требования * высшее образование в области математики, статистики, экономики, информационных технологий или смежных областях * опыт работы с различными LLM * опыт разработки агентов * 3+ года Python, опыт async-сервисов, знание современных библиотек Data Science (Pandas, Scikit-Learn, TensorFlow) * глубокое знание SQL и оптимизации запросов * опыт prompt-инжиниринга и настройки LLM-инференса * глубокое понимание принципов построения мультиагентных систем и опыта их практического внедрения * знание методик когортного анализа и умение строить прогнозы финансовых показателей портфелей малого и микробизнеса. Будет преимуществом: * опыт руководящей должности от двух лет в проектах, связанных с обработкой больших данных, построением аналитических моделей и управлением рисками * понимание специфики сектора малого и микробизнеса в российских условиях, особенностей финансового поведения данной категории заемщиков * наличие успешных кейсов разработки AI-продуктов и решений для кредитных организаций * опыт реализации крупных интеграционных проектов в финансовой сфере * навыки работы с зарубежными аналоговыми системами и технологиями в рамках международного сотрудничества. Условия * комфортный современный офис рядом с м. Кутузовская / Тульская * офисный формат работы * ежегодный пересмотр зарплаты * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте (для Junior позиций) * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Команда занимается разработкой NLP-моделей и решений на основе LLM для операционных рисков. Решаем задачи по построению AI-агентов и мультиагентных систем. Примеры: система гардрейлов (в том числе на Jev-like моделях) для защиты LLM и AI-агентов Сбера от промпт-атак и джейлбрейков агентная память банка на базе темпорального графа знаний, учитывающего версии и изменения внутренней документации во времени Harness, который автономно превращает DS-код в продакшн-сервис — от сборки проекта по стандарту и интеграций до деплоя на стенды и тестирования AI-агенты для оценки рисков компаний экосистемы Сбера по внутренним документам, новостям, отчетности и описанию бизнес-процессов мультиагентная система по анализу репозиториев кода на уязвимости бизнес-логики и сверки с требованиями AI-агент DataEngineer для автономного анализа данных, поиска ошибок и аномалий, создания проверок качества данных, создания выгрузок и витрин. Задачи: — разработка PoC и MVP AI-агентов с использованием фреймворков для работы с LLM — адаптация и дообучение LLM на внутренних и внешних данных (SFT, LoRA, DPO, GRPO) — участие в выводе AI-агентов в продакшн, их интеграции и мониторинге качества — написание качественного кода и обсуждение технических решений с командой. Требования: — неоконченное высшее техническое образование с хорошей подготовкой в области математики и алгоритмов — знание Python, статистики и ML/DL-стека — опыт проектирования и обучения нейросетей: от CNN/RNN до трансформеров — опыт построения RAG-систем (классический RAG, GraphRAG, Agentic RAG) — опыт разработки автономных AI-агентов (LangChain, LangGraph, Deep Agents) — знание классических алгоритмов и структур данных, принципов ООП и паттернов проектирования — умение писать чистый и поддерживаемый код (Clean Architecture, SOLID) — опыт работы с Git. Преимуществом будет: — опыт работы с FastAPI, Docker, vLLM, Unsloth, Langfuse — успехи в соревнованиях (например, Kaggle Expert+ или призовые места на хакатонах) — вклад в open-source проекты, публикация моделей и датасетов на Hugging Face — погружённость в тренды индустрии, интерес к свежим статьям и новым инструментам — знание других языков программирования (например, C++, Java). Условия: — оплачиваемая стажировка — возможность выбрать удобный график – офис или гибрид (локация: м. Кутузовская) — корпоративный спортзал и зоны отдыха.

Задачи: — Разрабатывать и поддерживать backend-сервисы на Python (FastAPI, asyncio) — Развивать логику диалогового агента: сценарии и состояния звонка, извлечение данных из реплик, работу с LLM — Писать интеграции с МИС и CRM клиник (расписание врачей, запись пациентов) и с внешними API — Проектировать REST API для веб-интерфейса операторов и админки клиник — Работать с фоновыми задачами и очередями (Celery / TaskIQ, RabbitMQ, Redis) — Проектировать схемы данных и оптимизировать запросы в PostgreSQL — Разбирать логи звонков, находить и исправлять ошибки в поведении агента — Участвовать в код-ревью, покрывать код тестами, улучшать кодовую базу — Деплоить и сопровождать сервисы в Docker на Ubuntu Требования: — Коммерческий опыт разработки на Python от 1 до 3 лет — Опыт разработки на FastAPI и уверенное понимание async/await в Python — Pydantic (v2): валидация данных, схемы запросов и ответов, настройки — PostgreSQL: уверенный SQL, индексы, оптимизация запросов — Опыт с SQLAlchemy — Alembic: написание и сопровождение миграций — Celery: фоновые и отложенные задачи, ретраи, периодические задачи — RabbitMQ: понимание очередей, exchange и routing, гарантий доставки — Redis: кэширование, брокер/бэкенд для задач, TTL, базовые структуры данных — Docker и Docker Compose: умение собрать образ, поднять окружение из нескольких сервисов и разобраться, почему оно не стартует — Git и pull request-флоу (GitHub) — Linux (Ubuntu) на уровне повседневных задач: логи, процессы, сеть — Умение читать чужой код, задавать вопросы и доводить задачи до прода Будет плюсом: — Опыт интеграции с LLM API (OpenAI-совместимые API, vLLM), промпт-инжиниринг, structured output — Знакомство с LangGraph, LangChain или другими фреймворками для агентов — Опыт с real-time системами: WebSocket, стриминг, работа с аудио — Знакомство с IP-телефонией (SIP, Asterisk, LiveKit) или с медиапайплайнами (Pipecat) — Опыт интеграций с медицинскими информационными системами — Тесты на pytest как привычная часть работы Условия: — Зарплата 80 000–120 000 ₽ на руки, конкретная сумма — по итогам собеседования — Удалённая работа — Оформление по ТК или как самозанятый — Короткий путь от задачи до прода, без месяцев согласований — Прямое влияние на технические решения: открытость к предложениям по стеку и архитектуре

Команда «ИИ для сотрудников» отвечает за создание AI-инструментов для сотрудников компании: определение приоритетов, разработка решений и внедрение. Задачи: — Разрабатывать платформу для создания внутренних ассистентов с использованием современных подходов. — Обеспечивать доступ сотрудников к актуальным возможностям LLM — интеграция новых моделей, MCP и API. — Анализировать качество генеративных моделей, выявлять слабые места и предлагать способы их улучшения. — Следить за best practices и open source-проектами в области LLM и интеграций. — Разворачивать и поддерживать сервисы в Kubernetes: деплой, конфигурация, настройка мониторинга и алёртов. — Проводить code review и архитектурные ревью, вносить предложения по оптимизации и развитию. Требования: — Опыт разработки от 5 лет. — Активное использование LLM, понимание их возможностей и ограничений. — Опыт деплоя сервисов в продакшн и обеспечение их стабильной работы. — Приветствуется понимание архитектуры AI-агентов (оркестрация, memory, tools). — Опыт работы с фреймворками для LLM-пайплайнов: LangChain, LlamaIndex, Haystack. Условия: — Аккредитованная IT‑компания. — Возможна удалённая работа по РФ. — При желании гибридный формат (офисы в Москве, Санкт-Петербурге, Новосибирске). — Нет дресс‑кода и строгого графика. — Участие в конференциях, ДМС и забота о здоровье сотрудников.