Вакансии

Задачи: — Проектировать и разрабатывать AI-агентов: реализовывать сценарии, оркестрацию, tool calling и интеграции с внутренними сервисами — Превращать прототипы и модели Data Science в надёжные промышленные решения: упаковывать, встраивать в сервисы и пайплайны, версионировать и сопровождать — Разрабатывать на Python сервисы, библиотеки и пайплайны, писать автотесты и участвовать в код-ревью — Проектировать, строить и сопровождать витрины данных для ML-моделей и агентов — Обрабатывать большие объёмы данных с помощью SQL, Hadoop и Spark, оптимизировать запросы и вычислительные задачи — Обеспечивать качество и наблюдаемость решений: логирование, мониторинг, обработку ошибок и контроль качества данных Требования: — Опыт промышленной разработки на Python от 2 лет — Умение писать поддерживаемый код: типизация, тестирование, обработка ошибок, логирование и работа с зависимостями — Уверенное владение SQL: сложные запросы, оконные функции, оптимизация и работа с большими наборами данных — Опыт построения и сопровождения ETL/ELT-пайплайнов и витрин данных — Опыт вывода сервисов, моделей или data-продуктов в промышленную эксплуатацию — Понимание принципов работы AI-агентов: оркестрация, tool calling, управление состоянием и интеграция с API — Понимание жизненного цикла ML-модели — от артефакта и данных до инференса, мониторинга и обновления Будут плюсом: — опыт с LangGraph, LangChain, LlamaIndex или собственными оркестраторами — LLM, RAG и векторные хранилища — оркестрация в Airflow — Kafka и событийные архитектуры — Docker, CI/CD и Kubernetes — Hadoop и Spark — внедрение ML/LLM-сервисов во внутреннем контуре — опыт в банковском домене или продуктах для юридических лиц

Задачи: — Разрабатывать и улучшать RAG-приложения: chunking, embeddings, retrieval и reranking. — Строить LLM-воркфлоу и агентов с использованием LangChain, LangGraph, LlamaIndex, CrewAI или аналогичных. — Разрабатывать переписывание запросов, классификацию намерений, маршрутизацию и структурированное извлечение. — Интегрировать LLM API/модели в производственные системы. — Работать с OpenAI, Gemini, Hugging Face и PyTorch. — Вносить вклад в SFT, LoRA, QLoRA, PEFT и оценку LLM. — Работать с OCR, PDF/DOCX, STT и памятью для диалогов. — Создавать API с Python / FastAPI и работать с Docker, Git, тестированием и CI/CD. — Мониторить и улучшать качество моделей, задержки и стоимость. — Брать ответственность за отдельные функции/компоненты под наставничеством старшего инженера. Требования: — 2–4 года опыта в NLP / ML / Information Retrieval / Applied AI. — Сильные навыки Python. — Практический опыт с LLM / RAG / AI-agent приложениями. — Понимание трансформеров, embeddings, prompting, retrieval, chunking и reranking. — Опыт с LangChain / LangGraph / LlamaIndex / CrewAI. — Опыт с Qdrant / Pinecone / Milvus / Weaviate / Elasticsearch. — Опыт с OpenAI / Gemini / Hugging Face и знание PyTorch. — Понимание оценки LLM и снижения галлюцинаций. — Навыки FastAPI, REST, Docker, Git. — Многоязычный NLP будет плюсом. — Профессиональный английский. Желательно: — Знания SFT / LoRA / QLoRA / PEFT. — Знания BM25 / RRF. — Опыт с RAGAS / DeepEval / LangSmith / Langfuse. — Опыт с OCR/STT. — Опыт с Graph RAG. — Опыт с vLLM/TGI/Triton. — Оптимизация под GPU. — Знание корейского языка. Условия: — Работа в Инчхоне, Южная Корея. — Полная занятость. — Формат работы: офис / гибрид. — Конкурентная зарплата, обсуждается индивидуально. — Возможна поддержка с визой и релокацией.

Задачи: — Проектировать, разрабатывать и оптимизировать графы знаний на базе Neo4j. — Разрабатывать решения Graph-RAG для задач поиска, извлечения информации и использования AI. — Проектировать модели графовых данных, связи между сущностями и общие модели данных. — Создавать и автоматизировать пайплайны для загрузки, трансформации и обработки данных. — Работать со структурированными и неструктурированными наборами данных, включая профилирование, сбор данных, валидацию и проверки качества. — Разрабатывать масштабируемые потоки обработки данных с использованием AWS-сервисов. — Работать с потоковыми данными и обработкой на базе Kafka. — Участвовать в развитии архитектуры, управляемой метаданными, каталогов данных и принципов Data Fabric. — Участвовать в автоматизации data pipelines и разработке пайплайнов на базе AI-агентов. Требования: — Сильный практический опыт работы с Neo4j, включая моделирование графовых данных и оптимизацию запросов. — Практический опыт проектирования и реализации решений Graph-RAG. — Сильные навыки разработки на Python. — Опыт работы со stream processing и Kafka. — Практический опыт работы с технологиями обработки данных в AWS, включая AWS Glue, Data Catalog, Data Lake/Lake Formation и связанные сервисы. — Глубокое понимание data pipelines, data modeling и data architecture. — Опыт обработки как структурированных, так и неструктурированных данных. Будет преимуществом: — Опыт работы с PostgreSQL. — Сильный опыт построения data pipelines на AWS. — Опыт работы с LangChain, CrewAI или аналогичными агентными фреймворками. — Опыт автоматизации data pipelines с использованием AI-агентов. — Знание распространенных моделей данных, metadata-driven processing и принципов Data Fabric. — Опыт интеграции Knowledge Graphs с решениями на базе RAG/LLM. Требования по доступности: — Возможность частично пересекаться с рабочими часами по тихоокеанскому времени США (US Pacific Time), включая доступность до 11:00 AM PT. Условия: — Возможности для профессионального и карьерного развития. — Поддержка в получении профессиональных сертификаций. — Программа менторства. — Медицинская страховка. — Участие в корпоративных мероприятиях. — Гибкие форматы работы (гибридный или удалённый). — Оплачиваемый отпуск и больничные.

Location: Boston, MA Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done. ABOUT THE ROLE (Onsite in Boston) We’re looking to bring on an experienced frontend engineer to develop and enhance new features on LangSmith, our enterprise platform product for LLM application observability, testing, and debugging.   WHAT YOU WILL DO - Develop new user-facing features using React & Typescript - Build reusable components and front-end libraries for future use - Translate designs and wireframes into high-quality code - Optimize components for maximum performance across a vast array of web-capable devices and browsers - Collaborate with fullstack and backend developers and UX/UI designers, to enhance usability   WHAT YOU WILL BRING - Extensive front-end engineering experience with strong proficiency in React, JavaScript and TypeScript - Hands-on experience with front-end development tools like Babel, Vite, Webpack, NPM, and Yarn - Familiarity with REST APIs and experience working closely with fullstack and backend engineers - Knowledge of modern authorization mechanisms, such as JWTs - A passion for user experience, design and building clean, engaging, and intuitive interfaces - Strong written and oral communication skills, with the ability to explain technical concepts clearly and concisely to both technical and non-technical stakeholders - The DNA to thrive in a start-up, fast-paced environment. Views unstructured environments as an opportunity to figure out the most impactful work and help define the future success of the company - An ownership mind-set. You are self-driven and look for opportunities to make an impact. - Strong computer science fundamentals, ideally with a degree in computer science or related field   Annual salary range: $170,000-$235,000 USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Задачи: — Разрабатывать AI-агентов на Python — Интегрировать модели и внешние API — Работать с RAG и Tool Calling — Упаковывать решения в Docker — Разворачивать через Helm в Kubernetes — Работать с ArgoCD и анализировать логи — Исправлять ошибки и самостоятельно обновлять production-деплои — Работать с SCA-сканерами и зависимостями Требования: — 3–6 лет опыта, Middle / Senior — Python + LangChain / LlamaIndex / Semantic Kernel или аналоги — Docker, Kubernetes, Helm — Bash, debugging, анализ логов — Понимание AI Agents, RAG, Tool Calling — Глубокое понимание Python-зависимостей (pip, poetry, virtualenv) — Высокая самостоятельность: от кода до запуска в production Условия: — Формат работы: Remote — Занятость: Full-time — Зарплата: до 260 000 ₽ на руки — Оформление: ИП / СМЗ — Локация: РФ, гражданство РФ

Location: Remote - US About MariaDB Are you ready to shape the infrastructure for a groundbreaking cloud database platform? MariaDB is making a big impact on the world. Whether you’re checking your bank account, buying a coffee, shopping online, making a phone call, listening to music, taking out a loan or ordering takeout – MariaDB is the backbone of applications used every day. Companies small and large, including 75% of the Fortune 500, run MariaDB, touching the lives of billions of people. With massive reach through Linux distributions, enterprise deployments and public clouds, MariaDB is uniquely positioned as the leading database for modern application development. MariaDB Cloud is revolutionizing the way businesses manage their data in the cloud. We're on a mission to provide a seamless, powerful, and user-centric Database-as-a-Service (DBaaS) solution that sets new industry standards. MariaDB Cloud is a Multi-cloud Database-as-a-Service platform offering, specializing in managing MariaDB, one of the most popular open-source relational database systems. MariaDB Cloud is more than just a database; it's a comprehensive and innovative platform that delivers continuous availability, autonomous operation, and serverless capabilities. We're redefining the database experience to make it more accessible, intuitive, and efficient than ever before. The Opportunity About the Role We are seeking a Senior Software Engineer to join a small, high-ownership team building robust cloud infrastructure for our globally deployed relational database engine. You will design and implement the application layers, business logic, and API workflows at the intersection of data management, cloud operations, and relational database services—critical components that enterprise applications depend on for correctness, performance, and governance. This is a hands-on, end-to-end role. You will lead features from whiteboard to production: owning implementation, writing clean and scalable code, and leveraging deep expertise in cloud orchestration to ensure robust deployment across multi-cloud environments. Proficiency in Terraform is essential for managing our complex infrastructure and ensuring reproducible deployments. Our team is small by design, offering high individual impact and direct visibility into engineering strategy. The ideal candidate has scaled infrastructure across multiple cloud providers and approaches every challenge with an ownership mindset—shipping production-grade systems that set new industry standards. Key Responsibilities 8+ years of experience in software development with deep expertise in distributed systems and cloud-native architecture. Proven track record of designing and operating multi-cloud infrastructure at scale across AWS, GCP, and Azure. Advanced proficiency in Terraform for Infrastructure as Code (IaC) and complex cloud orchestration. Expertise in containerization and orchestration using Kubernetes and Docker in production-grade environments. Experience building and maintaining robust CI/CD pipelines for automated, scalable deployments. Strong command of Go or Python for developing cloud-native applications, controllers, and automation tooling. BS/MS in Computer Science or a related technical field. Experience designing and implementing REST and/or gRPC APIs used by external consumers or frontend systems. Knowledge of MariaDB server is a plus. Required Qualifications Experience 5+ years of professional software development experience, with at least 2 years focused on data-intensive applications, database-adjacent features, or enterprise software. Demonstrated end-to-end ownership of a non-trivial production feature or module: you have taken something from functional specification through implementation to production. Hands-on production experience deploying, debugging, and managing application services across at least two major public clouds (AWS, Azure, GCP). Experience building or integrating with vector databases, embedding pipelines, semantic search, or retrieval-augmented generation (RAG) systems. Prior work building LLM-based applications or workflows using AI agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, or equivalent). Technical Skills Strong proficiency in Python for AI application development, alongside high proficiency in at least one backend language (such as Go, Java, or C++). Deep, practical application of relational databases: writing complex SQL, structural indexing, query optimization, transactions, and schema design. Experience implementing temporal data models, event-driven tracking, audit logs, or utilizing system-versioned tables. Solid understanding of application security fundamentals: authentication, authorization, RBAC, row-level security, and multi-tenant data isolation at the application layer. Proficiency with modern application monitoring tooling: structured logging, distributed tracing (OpenTelemetry), and functional alerting. Experience designing and implementing REST and/or gRPC APIs used by external consumers or frontend systems. Preferred Qualifications Experience contributing to or maintaining open-source projects in the database, AI/ML application, or agent-tooling ecosystem. Hands-on experience developing MCP servers or integrating A2A (Agent2Agent) protocols into production systems. Familiarity with agent memory frameworks such as Mem0, Graphiti, or Letta. Experience leveraging graph databases or multi-hop graph traversal (Neo4j, Kuzu, FalkorDB, or similar) within applications. Background with in-memory data grids or semantic caching systems (Redis or similar) to optimize AI token usage. Experience packaging applications for on-premises or air-gapped deployment environments, including regulated infrastructure. Knowledge of MariaDB or MySQL application development, system-versioned tables, or temporal SQL. Location & Job Info Full-Time - Remote - US Why Join Us Compensation The annual anticipated U.S. base salary range for this full-time position is $141,000-$185,000. Actual salaries may vary and fall outside of this range depending on factors such as a candidate’s qualifications, geographic location, skills, experience, and competencies. In addition, we are often open to a wide variety of profiles, and recognize that the person we hire may be less experienced (or more senior) than the job description as posted. Salary is one component of MariaDB’s total rewards package.  We provide a competitive benefits package. Additional benefits includes health insurance, life, and disability insurance, funds toward professional development resources, Flexible Paid Time Off (FPTO), paid holidays, 401(k), and parental leave, a massive degree of flexibility and freedom, just to name a few! Salaries for candidates outside the U.S. will vary based on local compensation structures. How to Apply If you are interested in this position, please submit your application along with your CV/Resume through our ATS Greenhouse on our MariaDB Careers site. At MariaDB, we celebrate the diverse experiences and perspectives of our employees because this drives innovation and success. MariaDB is an equal opportunity employer dedicated to creating a welcoming and inclusive workplace for everyone. MariaDB does not sponsor work visas or relocation. MariaDB is committed to providing accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter. MariaDB will not accept agency resumes without a prior contractual agreement with HR. Please do not forward resumes to any recruiting alias or employee directly. MariaDB is not responsible for paying any fees associated with any unsolicited submitted CV/Resumes.

В рамках данной позиции предстоит заниматься проектированием и разработкой AI-агентов, включая реализацию сценариев, оркестрацию, вызов инструментов и интеграцию с внутренними сервисами. Необходимо преобразовывать прототипы и модели Data Science в стабильные промышленные решения, обеспечивая их упаковку, интеграцию в сервисы и пайплайны, версионирование и сопровождение. Работа включает разработку на Python сервисов, библиотек и пайплайнов, написание автотестов и участие в код-ревью. Также требуется проектировать, создавать и поддерживать витрины данных для ML-моделей и агентов, обрабатывать большие объемы данных с использованием SQL, Hadoop и Spark, оптимизировать запросы и вычислительные задачи. Важной частью является обеспечение качества и наблюдаемости решений через логирование, мониторинг, обработку ошибок и контроль качества данных. Кандидат должен иметь опыт промышленной разработки на Python не менее двух лет, уметь писать поддерживаемый код с типизацией, тестированием, обработкой ошибок и логированием, а также уверенно владеть SQL, включая сложные запросы, оконные функции и оптимизацию. Требуется опыт построения и сопровождения ETL/ELT-пайплайнов и витрин данных, а также опыт вывода сервисов, моделей или data-продуктов в промышленную эксплуатацию. Необходимо понимание принципов работы AI-агентов, включая оркестрацию, вызов инструментов, управление состоянием и интеграцию с API, а также жизненного цикла ML-модели от артефакта и данных до инференса, мониторинга и обновления. Будет преимуществом опыт разработки AI-агентов с использованием LangGraph, LangChain, LlamaIndex или собственного оркестратора, а также опыт работы с LLM, RAG и векторными хранилищами. Дополнительным плюсом считается опыт оркестрации пайплайнов в Airflow или аналогичных системах, работы с Kafka и событийными архитектурами, знание Docker, CI/CD и Kubernetes, практический опыт с Hadoop и Spark, внедрение ML- или LLM-сервисов во внутреннем контуре, а также опыт в банковском домене, продуктах для юридических лиц или документообороте. Формат работы — офис или гибрид в Москве.

Location: London Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. About LangChain At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. Today, LangChain, LangGraph, LangSmith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. About the role: Location: Hiring in London,UK We're looking for a Commercial Account Executive to join our growing GTM team and help drive adoption of LangChain's products. In this role, you'll support the sales process by helping technical teams at innovative companies discover, evaluate, and adopt LangChain's solutions. This is a great opportunity to learn and grow in a high-impact environment where you'll work alongside experienced sales, engineering, and customer success professionals. If you're eager to build relationships, learn about cutting-edge GenAI technology, and contribute to our go-to-market efforts, we'd love to hear from you. WHAT YOU’LL DO: - Support the full sales cycle from initial outreach through close, with strong attention to follow-up and follow-through - Build relationships with prospects and customers, understanding their needs and how our products can help - Prospect strategically into SMB, mid-market, and small-enterprise accounts How to be successful in this role: - 3+ years of experience in sales, business development, or a customer-facing role (SaaS or tech preferred) - 1+ year of experience in a closing role for SMB, commercial, and mid-market accounts - Strong track record of sourcing, qualifying, and closing outbound deals - Execution mindset: you excel in fast-paced environments and move with urgency - Curious and solution-oriented: you’re always finding ways to level-up your understanding of technology, your customer’s business, and your craft. - Self-starter and resourceful; you find ways to solve problems and move things forward Compensation & Benefits: - We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include things like medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Задачи: — Руководить проектированием и развитием масштабируемых распределённых систем на Go — Отвечать за интеграцию и конфигурацию AI — от выбора моделей и интеграции API до prompt engineering и развертывания в продакшене — Переводить бизнес-цели в технические дорожные карты и планы поставки — Проводить архитектурные и код-ревью, наставлять старших инженеров — Представлять инженерную команду на встречах с внешними заинтересованными сторонами и оспаривать нереалистичные требования — Улучшать процессы CI/CD, наблюдаемости и инженерные процессы — Балансировать между кодированием, руководством командой и архитектурным контролем Требования: — 5–6+ лет коммерческого опыта с Go — 2+ года в роли технического лидера или ведущего инженера — Глубокие знания распределённых систем, микросервисов и конкурентности в Go (goroutines, каналы, синхронизация) — Практический опыт внедрения, настройки или интеграции AI/ML решений в продакшн-бэкендах, включая работу с LLM, RAG pipeline, OpenAI API, LangChain или аналогичными фреймворками — Опыт с CI/CD, облачной инфраструктурой и современными DevOps практиками — Высшее образование в IT — Готовность работать по CEST и использовать корпоративный ноутбук — Готовность выполнить live coding на Go во время интервью Условия: — Работа над долгосрочным продуктом с реальным бизнес-эффектом — Международная распределённая команда с коллаборативной культурой — Возможность взять ответственность за зрелое приложение — Гибкие условия работы и удалёнка

Location: Phoenix, AZ Workplace: Remote ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. About LangChain At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. Today, LangChain, LangGraph, LangSmith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust LangChain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. About the role: We’re looking for an experienced Account Executive to join our growing GTM team and drive the next wave of growth at LangChain. In this role, you’ll own the full sales cycle helping technical teams at cutting-edge enterprise companies discover, evaluate, and adopt LangChain’s products. This is a high-impact, high-visibility role where you’ll collaborate closely with engineering, product, and customer success teams to shape how GenAI is adopted across different domains. If you're a relationship-driven seller who thrives in fast-paced, unstructured environments and wants to be part of building the go-to-market motion from the ground up, we’d love to hear from you. WHAT YOU’LL DO: - Own and manage the full sales cycle, from lead stage to close with a strong bias for follow-up and follow-through - Act as a trusted advisor to prospects and customers, learning their needs and demonstrating how our products can solve real problems - Drive proof-of-concept efforts, working with sales engineers and product experts to demonstrate tangible value - Stay on top of product updates and industry trends to educate customers and shape the conversation around GenAI adoption - Build long-term customer relationships and support post-sale success in partnership with our deployment and support teams - Negotiate pricing and deal terms and work with legal to redline contracts - Bring customer insights back to product and engineering teams to inform the future roadmap and improve the user experience - Help define and iterate on our sales playbook - your input will help shape our go to market strategies How to be successful in this role: - 5+ years of experience selling complex software to enterprise customers - A relationship builder that garners trust quickly and approaches sales with empathy and curiosity - Excellent communication and storytelling skills - Technical acumen with the ability to establish credibility with technical decision makers. - You thrive in fast-paced start-up environments and are energized by impact - you want to help evolve the playbook, not just follow one - Driven and solutions-oriented; you know how to navigate roadblocks and keep deals moving forward - Passionate about generative AI and excited to help customers navigate a rapidly evolving industry Compensation & Benefits: - We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include things like medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. - Annual salary: $350K OTE USD COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: Remote: United States About Neo4j: Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy-to-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world’s largest graph community. Intelligence that works. Results that matter. Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business. Our Vision: At Neo4j, we have always strived to help the world make sense of data. As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we’re disrupting how organizations leverage their data to innovate and stay competitive. Job Title: Sr. AI Solutions Architect Job Summary: As a Sr. AI Solutions Architect, you will lead the design, construction, and deployment of AI solutions that combine the power of graph databases and AI. You will act as a trusted advisor to our strategic customers, guiding them through complex data challenges and delivering transformative business value. You will have a great understanding of graph technologies, LLMs, and AI frameworks, and will be able to translate customer needs into production-ready AI applications grounded in real-world context using Neo4j Knowledge Graphs. Key Responsibilities: Solution Architecting: You will engage with technical leaders, stakeholders, and strategic partners to shape and guide the successful implementation of Graph+GenAI solutions, serving as a trusted advisor and thought partner to decision-makers throughout complex, high-impact engagements. You will design and advocate for robust, scalable solution architectures, leading the deployment of Graph+GenAI systems aligned with complex enterprise requirements and industry best practices driving architectural excellence and long-term value. You will collaborate closely with customer leadership to deeply understand their strategic objectives, translating them into high-impact AI solutions that harness the synergy of graph databases and LLMs. This includes leading the discovery of requirements, assessing enterprise data ecosystems, and identifying innovative opportunities to apply graph-based AI at scale. You will lead stakeholder engagement across all levels to steer project scope, align on priorities, mitigate risks, and ensure timely delivery—ensuring alignment between technical execution and business outcomes while maintaining a focus on measurable impact Solution Engineering: You will develop, test, and deploy production-ready AI applications that integrate graph databases with LLMs and orchestration frameworks. This involves writing production-level code, optimizing for performance and scalability, and ensuring seamless integration with customer systems. Continuously evaluate and improve the performance, scalability, and efficiency of deployed AI applications, incorporating new techniques and technologies as they emerge. Education & Enablement: You will work with other teams at Neo4j (Product and Marketing) to influence the roadmap and provide insights from the field, and package approaches, best practices, and lessons learned into thought leadership, methodologies, and published assets. You will share your expertise internally with other Neo4j teams and also with customers through workshops, training sessions, and documentation to empower them to effectively utilize, maintain, and reproduce the AI solutions you deliver. Maintain continuous learning and stay up-to-date with the rapidly evolving GenAI landscape, proactively seeking knowledge of new trends and technologies. Required Qualifications: Enterprise Application Architecture: 7+ years of experience architecting and delivering enterprise-grade applications, with a deep understanding of the full software development lifecycle and the ability to guide teams through complex design and implementation decisions. LLM Proficiency: 2+ years of Experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, and integrating LLMs into applications. Maintain up-to-date knowledge of different LLM providers and their strengths and limitations, as well as open-source ones). Programming Proficiency: Advanced proficiency in at least one major programming language (e.g., Java, JavaScript, Python, or C#), with a proven track record of delivering clean, maintainable, and scalable code within complex systems. Deployment and Version Control: Deep hands-on experience with deployment tooling across Linux, Docker, and Kubernetes environments, along with expert-level use of version control systems (e.g., Git, SVN) in enterprise development workflows. Cloud Computing Expertise: Demonstrated expertise in deploying and scaling applications across cloud platforms (AWS, Azure, GCP), with a strategic understanding of cloud-native architecture and DevOps best practices. Generative AI Ecosystem Knowledge: In-depth knowledge of the generative AI ecosystem, including frameworks (e.g., LangChain, LlamaIndex, Haystack) and familiarity with cloud-native AI platforms (e.g., AWS Bedrock, Google Vertex AI, Azure ML), enabling you to architect end-to-end AI solutions. Data & Analytics Fluency: Strong background in data engineering, analytics, or data science, with the ability to design data pipelines and workflows across structured and unstructured data. Hands-on experience with big data technologies (e.g., Hadoop, Spark, Hive) and database systems (SQL and NoSQL) Graph Database Authority: Deep expertise in graph data modeling and query languages (e.g., Cypher), along with practical experience working with graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph) or triple stores (e.g., Ontotext, Stardog), enabling advanced knowledge graph applications Communication and Collaboration Skills: Exceptional communication and stakeholder engagement skills, with a demonstrated ability to influence cross-functional teams and align technical decisions with business goals. Problem-Solving and Analytical Abilities: Strong analytical mindset with the ability to break down complex problems, architect AI solutions, and mentor teams through implementation Willingness and ability to travel up to 50% to engage with customers, lead strategic discussions and ensure successful project execution. The annual on target earnings range for this position based in the United States is listed below. This range is an estimate, and the actual on target earnings may vary based on Neo4j’s compensation practices, job related skills, depth of experience, relevant certifications and trainings, in addition to geographic location. Based on the factors above, Neo4j utilizes the full width of the range. In addition to the range below, US employees are eligible for a stock option grant and certain roles are eligible for an annual bonus. Employees in this position are also eligible to participate in the Company’s standard benefit programs, which currently include the following: medical, dental, and vision benefits, 401(k), paid time off, and certain leaves of absence. Annual On Target Earnings Range $200,000 — $265,000 USD Why Join Neo4j? Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j. Neo4j is one of the fastest-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years. Raised the biggest funding round in database history ($325M Series F). Backed by world-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well-funded database companies in history. 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific , BT Group , Caterpillar , Cisco , Comcast , Department for Education UK , eBay , NBC News , Novo Nordisk , Worldline , and others . Co-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success. Countless industry awards . Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform. A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers. Research shows that members of underrepresented communities are less likely to apply for jobs when they don’t meet all the qualifications. If this is part of the reason you hesitate to apply, we’d encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues. One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career-defining experience. We look forward to receiving your application. Neo4j Values: Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values: (we)-[:VALUE]->(relationships) (we)-[:FOCUS_ON]->(userSuccess) (we)-[:THRIVE_IN]->(:Culture {type: [‘Open’, ‘Inclusive’]}) (we)-[:ASSUME]->(:Intent {direction:’Positive’}) (we)-[:WELCOME]->(:Discussions {nature: ‘IntellectuallyHonest’}) (we)-[:DELIVER_ON]->(ourCommitments) Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide. More information at www.neo4j.com . ©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.

Задачи: — Разрабатывать платформу для создания внутренних ассистентов с использованием современных подходов. — Обеспечивать сотрудникам доступ к актуальным возможностям LLM — интеграция новых моделей, MCP и API. — Анализировать качество генеративных моделей, выявлять слабые места и предлагать способы их улучшения. — Постоянно держать руку на пульсе: 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 существующих решений, вносить предложения по оптимизации и развитию. Требования: — Опыт разработки от 5 лет. — Активный пользователь LLM, применяешь их в работе и личной жизни, понимаешь возможности и ограничения. — Опыт деплоя сервисов в продакшн и обеспечение их стабильной работы. Будет преимуществом: — Понимание архитектуры AI-агентов (оркестрация, memory, tools). — Опыт работы с одним из фреймворков для написания LLM пайплайнов: LangChain, LlamaIndex, Haystack. Условия: — Работа удалённо по РФ, возможен гибридный формат в офисах Москвы, Санкт-Петербурга и Новосибирска. — Нет дресс-кода и строгого графика. — Современные технологии, участие в конференциях. — ДМС и забота о здоровье сотрудников.

В рамках данной позиции необходимо разрабатывать и внедрять механизмы изоляции и контроля для агентных систем, включая создание песочниц для выполнения инструментов, строгую изоляцию контуров, установку лимитов на действия и реализацию функции аварийного отключения агентов. В задачи входит настройка детального аудита логов действий агентов в продакшене, а также разработка и настройка системы мониторинга аномального поведения с оповещениями о подозрительных паттернах. Требуется проводить моделирование угроз и threat modeling для новых сценариев агентов на этапе проектирования, а также разрабатывать архитектурные решения и фильтры для защиты от атак на LLM, таких как prompt injection, data exfiltration и jailbreak, включая внедрение строгой валидации выходных данных агентов. Необходимо организовывать и проводить атаки на собственные агентные системы для выявления уязвимостей до их эксплуатации злоумышленниками, а также проводить аудит безопасности агентных пайплайнов от команды LLM-инженеров до продакшен-выкатки. Контроль исходящих данных включает обеспечение соблюдения политик по использованию данных и инвентаризацию всех потоков информации, направляемой во внешние LLM-провайдеры. Важной частью работы является настройка IAM и управление секретами, проектирование и внедрение ролевой модели доступа для агентов, определение прав доступа и безопасное хранение с ротацией секретов. Также требуется опыт использования сканеров исходного кода (SAST/DAST), включая подбор, пилотирование, установку, администрирование и интеграцию в CI/CD-конвейер, а также помощь разработчикам в расшифровке результатов и устранении уязвимостей. Кандидат должен уверенно владеть Python, иметь глубокие знания Docker и Linux, а также понимание принципов работы CI/CD. Необходим практический опыт threat modeling, работа с системами управления секретами (например, HashiCorp Vault), внедрение IAM-моделей, настройка песочниц и изолированных окружений. Важно понимание работы LLM и агентных систем, опыт с фреймворками LangChain, LangGraph, MCP и знание принципов Claude Code. Кандидат должен быть готов не только проводить ревью кода, но и самостоятельно проектировать и создавать безопасных агентов.

Neoai IT Center izlayapti mutaxassislarni bir nechta yo'nalishlarda: backend dasturlash (python, Django, Fastapi, Node.js, Express.js, Java, Spring Boot, PHP, Laravel, Rest API, Postgresql, Mysql, Mongodb, Git, Docker), frontend (HTML, CSS, JavaScript, TypeScript, React, Next.js, Vue.js, Angular, Tailwind CSS, Bootstrap, Git), mobil dasturlash (Dart, Flutter, Kotlin, Java, Swift, React Native, Firebase, Android Studio, Xcode, Git), UX/UI dizayn (Figma, Adobe XD, Photoshop, Illustrator, Figjam, UI Design, UX Research, Wireframing, Prototyping, Design System), kiberxavfsizlik (Linux, Python, Bash, Networking, TCP/IP, Wireshark, Nmap, Burp Suite, Metasploit, OWASP, Git, Docker), data science (Python, Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, PyTorch, SQL, Jupyter Notebook, Git), data analitika (Python, Pandas, Numpy, SQL, Excel, Power BI, Tableau, Matplotlib, Seaborn, Jupyter Notebook), sun'iy intellekt (Python, PyTorch, TensorFlow, Keras, Scikit-learn, Hugging Face, OpenAI API, Langchain, Numpy, Pandas, Git, Docker), robototexnika (Python, C++, Arduino, ESP32, Raspberry Pi, ROS, Matlab, Arduino IDE, OpenCV, Sensors, Git). Ish joyi Toshkent shahrida, ish vaqti 3 soat, maosh 4-5 million so'm. Vazifalar: o'quvchilarga zamonaviy IT texnologiyalarini nazariya va amaliyot asosida o'rgatish, darslarni amaliy loyihalar va real vazifalar bilan tashkil etish, o'quvchilar bilan individual ishlash va ularning rivojlanishini kuzatib borish, dars materiallari va amaliy topshiriqlar tayyorlash. Nomzodlar mas'uliyatli, kommunikativ bo'lishi, o'z yo'nalishi bo'yicha amaliy tajribaga ega bo'lishi, yangi texnologiyalarni muntazam o'rganib borishi talab etiladi. Shuningdek, tajribali mutaxassislar bilan birga professional muhitda rivojlanish imkoniyati mavjud.

Задачи: — проектировать и разрабатывать backend-сервисы и RESTful API (Python/Django) — строить agentic-пайплайны на LangChain, LangGraph, n8n и аналогичных инструментах — собирать и обрабатывать данные из внутренних систем (jira, confluence, продуктовые реестры, аналитические платформы) — строить ETL/ELT-пайплайны на Python/Pandas/NumPy, в том числе потоковые данные через Kafka — готовить данные для обучения/дообучения моделей и базово настраивать/оценивать качество ML/LLM-компонентов совместно с профильными специалистами — разворачивать и эксплуатировать сервисы в закрытом контуре (production-инфраструктура внутри периметра безопасности, без выхода во внешний интернет): Docker, Kubernetes, CI/CD — принимать архитектурные решения по сервисам и агентным пайплайнам — тестирование, код-ревью — взаимодействовать с продуктовыми и инженерными командами при формировании требований и внедрении агентных и аналитических инструментов в рабочие процессы — готовить дашборды и рекомендации для управленческих решений по приоритизации и балансировке портфеля продуктов. Требования: — от 3 лет опыта в backend-разработке с выводом решений в production — опыт построения агентных систем — обязателен — опыт самостоятельного проектирования архитектуры backend-сервисов и агентных пайплайнов, готовность нести ответственность за принятые технические решения — Python — продвинутый уровень; Django, RESTful API — SQL, опыт работы с реляционными и NoSQL хранилищами — LangChain, LangGraph, MCP servers, опыт с n8n или аналогами; понимание RAG-архитектур, embeddings, векторных баз данных — Pandas, NumPy, Kafka — обработка табличных и потоковых данных, ETL/ELT на больших объемах — Docker, Kubernetes, CI/CD — уверенный пользовательский уровень. Условия: — комфортный современный офис рядом с м. Кутузовская — гибридный формат работы — ежегодный пересмотр зарплаты, квартальная и годовая премия — корпоративный спортзал и зоны отдыха — более 400 образовательных программ СберУниверситета для профессионального и карьерного развития — программа адаптации и помощь руководителя на старте — 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.

В данной роли предстоит заниматься разработкой и проектированием AI-агентов, которые автоматизируют процессы отчетности и выявления отклонений в бизнес-процессах. Задачи включают создание пайплайнов для обнаружения аномалий, интеграцию этих решений с большими языковыми моделями (LLM) для генерации текстовых отчетов, а также работу с большими данными: подготовку признаков, агрегацию и передачу данных в модели. Кроме того, необходимо оптимизировать, упаковывать и разворачивать агентные системы в продуктивной среде. Важной частью работы является тесное взаимодействие с бизнес-подразделениями и аналитиками для преобразования их требований в технические AI-решения. Кандидат должен иметь не менее трех лет опыта коммерческой разработки на Python, хорошо разбираться в языковых моделях, включая понимание их ограничений, а также иметь опыт работы с LangChain или аналогичными инструментами для интеграции LLM в бизнес-задачи. Требуется знание математической статистики и основ различных методов машинного обучения, а также понимание принципов DevOps и процессов CI/CD. Работа предполагает офисный формат в Москве.

Location: Los Angeles, USA Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential, from efficient business platforms to next-generation AI capabilities. As a Staff Data and AI Engineer on the Enterprise Data & AI team, you will build the governed data foundations that power enterprise analytics, automation, and applied AI. Your primary focus will be data architecture and data engineering, where you will be responsible for designing and operating production data pipelines in Databricks, integrating & curating data through a medallion architecture, and defining semantic and domain models in Unity Catalog with subject-matter experts. You will also build agents and retrieval-augmented applications directly on this governed data using Databricks capabilities such as Unity Catalog functions, Mosaic AI, and model serving. You will own your systems from architecture through production operations, applying strong engineering, data quality, security, evaluation, and observability practices throughout The ideal candidate is a hands-on data and software engineer who can turn ambiguous enterprise needs into reliable data products and focused AI-enabled solutions. The role will report to the Senior Manager, Enterprise Data and AI. Responsibilities: Data Pipelines and Semantic Layer (Primary) Build and operate pipelines that ingest source-system data into Databricks using a medallion architecture across bronze, silver, and gold layers, with lineage, provenance, access controls, and logging. Partner with subject-matter experts to define and evolve domain, ontology, and semantic models in Unity Catalog, then curate reliable data products into those models. Own data quality for the pipelines and products you build, implementing tests, monitoring, and alerting so issues surface before they reach downstream consumers. Integrate enterprise systems through APIs, events, and middleware while applying appropriate authentication, retries, reconciliation, and auditability. Agents on the Databricks Data Layer (Secondary) Build production agents and retrieval-augmented applications grounded in governed Databricks data, using Unity Catalog functions as tools and Mosaic AI or model-serving capabilities. Add authorization, approvals, and auditability for consequential actions performed through agents or AI-enabled orchestrations. Create evaluation sets and observability for the agents you ship, including groundedness, latency, cost, and production feedback, and use the results to improve them. Engineering Fundamentals Apply solid engineering practices, including automated testing, code review, CI/CD, documentation, on-call support, and concise design notes. Own architecture decisions for your systems and communicate material tradeoffs clearly to technical and business partners. Required Qualifications: Bachelor’s degree in a related field, or equivalent practical experience. 8+ years of experience in data engineering, software or application engineering, or an adjacent enterprise technology role. Hands-on experience building production data pipelines, including ingestion, semantic or domain modeling, data quality, and lineage. Hands-on Databricks experience, including Unity Catalog, medallion architecture, and Mosaic AI or model serving. Hands-on experience shipping production AI solutions, such as retrieval-augmented, conversational, or agentic applications, grounded in data pipelines you built. Proficiency in Python and strong software engineering fundamentals, including testing, code review, CI/CD, deployment, and production support. Experience integrating enterprise systems through APIs, events, or middleware, including authentication, retries, reconciliation, and auditability. Experience with an LLM or cloud AI platform such as Vertex AI, Anthropic, or OpenAI and an automation platform such as Workato, n8n, Camunda, or similar. Working knowledge of data governance, privacy, and security practices applied to production data pipelines and products. Desired Qualifications: Experience with the Mosaic AI Agent Framework or Unity Catalog functions used as agent tools. Experience with enterprise or federated search, vector databases, or permission-aware retrieval. Experience with agent frameworks such as LangGraph, Pydantic AI, LangChain, or CrewAI. Experience with LLM or agent evaluation and observability, such as MLflow tracing. Experience with dimensional modeling or master data management. Experience integrating Workday, ServiceNow, Coupa, Concur, Oracle, OneStream, or Ironclad. Experience with GCP or AWS data services, dbt, containers, or serverless computing. For this role, you’ll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes your fellow Rioters, who are the customers of your work. Being a dedicated fan of games is not necessary for this position. Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Задачи: • Формировать AI-стратегию и roadmap бизнес-направления. • Находить наиболее перспективные точки применения AI. • Самостоятельно запускать первые прототипы и AI-пилоты. • Быстро проверять гипотезы и показывать первые измеримые результаты. • Создавать AI-агентов, RAG-решения и AI-автоматизации. • Формировать и приоритизировать портфель AI-инициатив. • Совместно с FDE и AI-командой доводить успешные пилоты до production и масштабирования. • Переосмысливать бизнес-процессы с учетом возможностей AI. • Работать с руководителями бизнес-направлений и защищать AI-инициативы. • Следить за развитием AI-технологий и применять новые подходы в реальных бизнес-кейсах. Требования: • 7+ лет профессионального опыта в технологиях, AI/ML, разработке продуктов или digital transformation. • Практический опыт запуска AI/ML или технологических продуктов. • Сильное понимание LLM, Generative AI, AI Agents и RAG. • Способность самостоятельно собрать работающий AI-прототип. • Product mindset и ориентация на быстрый результат. • Стратегическое мышление и способность формировать AI-roadmap. • Умение связывать технологии с конкретным бизнес-эффектом. • Опыт работы с руководителями и топ-менеджментом. • Высокая самостоятельность и ответственность за результат. AI-инструменты и технические навыки: • Практический опыт работы с OpenAI, Claude, Gemini, Llama, Qwen или аналогичными LLM. • Работа с LLM API, Prompt Engineering, Tool/Function Calling. • Создание AI-агентов с использованием LangChain, LangGraph, LlamaIndex или аналогов. • Практическое понимание RAG, embeddings, vector databases. • Использование AI coding-инструментов: Codex, Cursor, Claude Code или аналогов. • Python, REST API, SQL — на уровне, достаточном для самостоятельного создания прототипов. • Опыт работы с n8n, Make или другими инструментами автоматизации. • Понимание принципов работы и ограничений LLM, стоимости моделей и вопросов безопасности корпоративных данных. Кого мы ищем: • Не классического бизнес-аналитика или координатора. • Нужен человек, который способен: понять бизнес → увидеть возможность применения AI → самостоятельно собрать первый пилот → показать результат → сформировать roadmap → масштабировать решение вместе с командой. Главный результат: • Быстрые первые победы и последующая системная AI-трансформация бизнес-направления.