Вакансии

Задачи: — Реализация, операционная поддержка и внедрение инноваций в рамках гибридного облака (Private/Public Cloud, PaaS, Private AI platform). — Администрирование приватного облака и AI-платформы (MLOps, GPU-инфраструктура). — Поддержка PaaS (контейнеризация, Drupal, Moodle, Redis, MySQL). — Оптимизация публичных облаков. — Автоматизация процессов (DevOps, IaC, CI/CD). — Мониторинг. — Участие в дежурствах 24/7 (включая выходные и праздничные дни по графику). Требования: — Магистр (без опыта) или бакалавр с опытом от 2 лет в сфере DevOps, облачного инжиниринга или управления платформами/инфраструктурой. — Опыт работы с AI/ML-платформами с открытым исходным кодом (vLLM, RAG DB) и локальной GPU-инфраструктурой. — Опыт управления Private Cloud на базе Open-Source систем (Proxmox/KVM). — Опыт с IaC, CI/CD и мониторингом. — Опыт работы в ООН, с публичными облаками (AWS, Azure, GCP) и сертификации (Azure/AWS/GCP, Terraform) будут преимуществом. Условия: — Гибридный формат работы во Валенсии, Испания. Языки: — Английский — свободный. — Знание других официальных языков ООН будет плюсом.

Организация и построение удобной и отказоустойчивой системы оркестрирования .и мониторинга голосовых и WEB сервисов.

Управлять командой DS и MLE: управлять бэклогом, развивать компетенции, проводить найм и растить сотрудников; Формировать стратегию и дорожную карту развития AI-решений для Управления зарплатных проектов;

Location: Москва Дивизион «Моделирование и исследование данных» ищет опытного специалиста в области Data Science в Блоке Сеть Продаж. Ищем опытного DS, который вместе с командой будет строить топовые AI-решения на современном технологическом стеке для привлечения клиентов на зарплатные проекты, приносящие пользу клиентам, сотрудникам и Банку. Большой объем информации разной природы и модальностей (табличные данные, текст и т.п.) вместе с развитой технической инфраструктурой позволяют разрабатывать широкую линейку моделей от классических алгоритмов до самых современных state of the art моделей. Обязанности * Обеспечивать полный цикл создания AI-продуктов: от исследования данных и прототипирования до промышленного внедрения и мониторинга; * Участвовать в полном цикле разработки ML-моделей: постановка задачи, сбор данных, выбор модельных подходов и архитектур, обучение моделей, подготовка артефактов для внедрения с последующим мониторингом; * Презентовать результаты исследований техническим и бизнес-командам; * Обрабатывать данные для обучения, расчета метрик и поверки гипотез; * Контролировать качество, надёжность и стабильность ML-решений и AI-агентов; * Оценивать финансовый эффект от AI-инициатив, подтверждать их вклад в бизнес-показатели Банка в ходе проведения пилотов. Требования * Опыт работы в банковском Data Science / ML от 3 лет; * Глубокое понимание полного цикла создания AI-решений: от сбора данных и построения моделей до эксплуатации и A/B-тестирования; * Техническая экспертиза в стеке: Python, SQL, ML, MLOps, LLM/Agents; * Понимание процесса разработки AI-решений и создания production-ready кода; * Высшее образование в области технических / физико-математических наук; * Опыт разработки моделей Machine, внедренных в реальный процесс; * Высокий уровень ответственности и самостоятельности; * Опыт разработки GenAI решений; * Опыт внедрения GenAI решений как плюс; * Навыки взаимодействия с контрагентами и оценки бизнес-эффектов как плюс; * Опыт менторинга и развития технических специалистов как плюс. Условия * График 5/2, современный офис в шаговой доступности ст. Кутузовская (метро и МЦК) * Режим работы - офис; * Достойный уровень вознаграждения (оклад + годовая премия), мотивирующая система оплаты труда, ежегодный пересмотр зарплаты; * Широкие возможности для профессионального развития: корпоративный университет и множество онлайн-программ обучения * Бесплатная подписка СберПрайм+, многочисленные скидки и бонусы от партнёров: СберМаркет, МегаМаркет, Самокат, Еаптека и др. * Расширенный социальный пакет, ДМС для сотрудников и их родственников, страхование жизни; * Многофункциональный корпоративный спортивный зал (бесплатно) и зоны отдыха; * Сильная, мотивированная на развитие команда и дружелюбная атмосфера.

Location: Сочи Центр Инноваций создает передовые решения на стыке технологий и бизнеса за счет фундаментальных знаний, примененных к рынку в виде прикладного исследования, которое рождает технологию, для дальнейшей интеграции в продукт. Мы реализуем полный цикл разработки: от генерации идей, проведения исследований, до создания прототипов, MVP и запуска продуктов и сервисов. Взаимодействуем с клиентами, партнерами, продуктовыми командами, маркетингом, университетами. Обязанности ### Обязанности * сопровождение и развитие автоматизированной системы (АС) AI Hub * коммуникация с пользователями АС * решение инцидентов и проблем * решение задач по развитию АС * поддержка смежных систем и команд (АС, агенты и т.п.) * участие в цикле разработки: от генерации идей, проведения исследований, до создания прототипов, MVP и запуска продуктов и сервисов. * взаимодействие со всеми членами команды и другими подразделениями, а также партнерами для достижения целей проектных инициатив. Требования ### Требования * опыт от 2 лет на позиции DevOps/SRE  * степень бакалавра или магистра по профильной специальности * опыт работы в поддержке и эксплуатации автоматизированных систем * знание методологий SDLC, Agile и соответствующих инструментов * понимание технологий контейнеризации и виртуализации, Cloud Native, микросервисной архитектуры, сетевых технологий * понимание объектно-ориентированного программирования и design patterns * уверенное владение инструментами контроля версий, такими, как Git * знания Linux и сетевых протоколов * опыт работы с CI/CD (GitLab/Jenkins/GitHub Actions) * навыки мониторинга и логирования (Prometheus, Grafana, VictoriaMetrics) и написания скриптов на Python/Bash. ### Будет плюсом * опыт настройки и сопровождения инференса, MLOps * навыки работы с генеративными AI-моделями, опыт создания AI-агентов и использования их в работе * владение английским для свободного чтения материалов в своей профессиональной области. Условия ### Условия * гибридный формат работы из офиса в г. Москва, Сочи или Санкт-Петербург или удаленный формат работы из данных локаций. * годовой бонус и ежегодный пересмотр * ДМС с первого дня + стоматологию и льготное страхование для семьи * корпоративный университет Сбера, внутреннюю образовательную платформу, участие в IT-конференциях * льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний. ###

Location: AU: Sydney (45 Clarence St) Workplace: Hybrid The role & impact As an Engineering Manager in our Machine Learning & AI team, you'll lead a high-performing, inclusive team of engineers building and operating AI-powered experiences and capabilities across Xero. The team's work spans traditional machine learning, from predictive models and intelligent automation through to LLM-powered and agentic AI experiences that bring new ways for customers to interact with and get value from Xero. - You'll lead engineering across a broad range of technology surfaces, from customer-facing AI experiences and intelligent product workflows to the backend services, platforms and infrastructure that power them. You'll help turn research, models and emerging AI capabilities into reliable, secure and scalable production products that make a real difference for millions of small businesses. - You'll balance predictable delivery with a relentless focus on engineering quality and system health, building our maturity across MLOps, LLMOps, observability, evaluation and the operational practices needed to run AI systems in production. As AI continues to evolve, you'll also champion AI-native engineering practices and help your team adapt how we design, build, test and operate software. - Working closely with senior individual contributors, including staff and principal engineers, you'll help shape technical direction while focusing on team growth, delivery excellence and sustainable engineering practices. This is a role for someone who wants to build both the systems and the people that enable Xero to deliver AI-powered products at scale. The team & how they connect You'll join Xero's Data & AI organisation, within the AI Products division, reporting to the GM of ML Engineering. The team partners closely with Applied Science, Data Engineering, Product and other engineering teams to bring AI capabilities from research and experimentation into production. Our scope goes beyond building and serving models. We build the end-to-end engineering capabilities that bring AI to life in Xero's products, spanning traditional ML-powered automation and predictions, conversational AI assistants, LLM-powered workflows and agentic experiences. This means working across the full stack, integrating AI into customer experiences and product workflows while building the front end & backend services, platforms and infrastructure needed to make those experiences safe, reliable and scalable. It's a collaborative, evolving space where you'll help translate advances in ML and generative AI into practical customer outcomes, while building the engineering foundations to support the next generation of AI-native products. The team is currently working on - Production ML and AI engineering: Building and operating the platforms, services and practices that support traditional ML models, generative AI and agentic systems in production. - JAX (Just Ask Xero): Powering Xero's conversational GenAI assistant and evolving the underlying capabilities to support increasingly sophisticated, context-aware and agentic experiences. - Intelligent financial workflows: Developing capabilities such as Automated Bank Reconciliation and smart document capture, using ML and AI to match transactions, extract information from bills and receipts, and automate complex tasks. - XeroForce: Building the platform for AI-powered workflows, enabling agents to plan and execute complex tasks across Xero’s products and supporting reliable, reusable agentic experiences at scale. - End-to-end AI product experiences: Integrating AI into customer-facing experiences and product workflows, alongside the backend services, orchestration and infrastructure that make them work. Where and how you can work We support a flexible working model that empowers you to balance your professional and personal life. You'll have the option to work in a hybrid capacity, combining the focus of remote work with the collaboration of our office spaces and team boost days to foster connection and alignment. Here are some of the things we're looking for, for this role - You'll bring a strong track record of leading, coaching and developing engineers, building team capability and creating an environment where people can do their best work. - You have strong technical judgement across architecture, system design and engineering trade-offs, and can guide decisions across a broad and evolving technology landscape. - You've translated business goals into executable engineering plans, balancing near-term delivery with long-term technical health and maintainability. - You're experienced in delivering roadmaps predictably in fast-paced, evolving environments, navigating ambiguity and adapting as priorities and technology change. - You understand what it takes to build and operate production-grade software, from customer-facing experiences and backend services through to distributed systems, platforms and infrastructure. - You stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team, support an incident or help resolve a complex technical challenge. - Experience with machine learning, generative AI or AI-powered products is a bonus rather than a requirement. You don't need to be an ML specialist, but genuine curiosity about the space and an appetite to learn how these technologies are changing software engineering will help you thrive here. Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Location: Dhahran, Saudi Arabia We are seeking an experienced Enterprise Architect to define, develop, and govern the enterprise architecture strategy, frameworks, standards, and roadmaps, ensuring alignment between business objectives, technology capabilities, and organizational priorities. The role will drive enterprise-wide architecture across computing and cloud platforms, communications and network infrastructure, data and artificial intelligence, cybersecurity, and business architecture. The successful candidate will provide architectural leadership, establish target-state architectures and reference designs, and ensure that enterprise solutions are scalable, secure, resilient, compliant, and aligned with the organization’s strategic goals. Key Responsibilities Define and maintain enterprise architecture frameworks, principles, standards, reference architectures, and technology roadmaps aligned with business strategy. Lead the design and evolution of enterprise technology architectures covering on-premises infrastructure, private and public cloud, high-performance computing (HPC), AI infrastructure, and enterprise platforms. Develop communications architecture across data networks, transmission networks, and satellite communications. Establish enterprise data architecture and governance frameworks, enabling trusted analytics, artificial intelligence, machine learning, and MLOps capabilities. Define and govern cybersecurity architecture to ensure the protection of enterprise systems, infrastructure, applications, and data in accordance with applicable security controls and standards. Translate business strategy, operating models, and business processes into architecture artifacts that support enterprise transformation and technology alignment. Provide architectural assurance and governance for major programs, projects, and technology solutions, ensuring compliance with approved standards and design principles. Identify technology opportunities, assess emerging technologies, and recommend solutions that enhance operational efficiency, scalability, resilience, security, and business value. Collaborate with business stakeholders, technology teams, cybersecurity specialists, and program leaders to ensure cohesive, integrated enterprise solutions. Maintain architecture documentation, identify capability gaps, and develop transition roadmaps to support the organization’s target-state architecture. Qualifications and Experience Bachelor’s degree in Computer Engineering, Computer Science, Business Management, or a related discipline; a Master’s degree is preferred. 10 to 15+ years of relevant professional experience in enterprise architecture, technology architecture, infrastructure, communications, data and AI, cybersecurity, or business architecture. Demonstrated experience developing enterprise architecture strategies, target-state designs, standards, governance frameworks, and implementation roadmaps. Strong understanding of enterprise technology environments, business capabilities, architecture governance, and technology-to-business alignment. Proven ability to lead cross-functional architecture initiatives and engage effectively with senior stakeholders. Excellent analytical, strategic planning, communication, and stakeholder management skills.

Location: New York; San Francisco Bay Area, California; Connecticut Workplace: hybrid Provectus is a Premier AWS Partner and an Anthropic Strategic Partner, featured in Forrester's AI Technical Services Landscape. We are roughly 420 engineers building production AI for enterprise clients in Financial Services, Insurance, Healthcare and Life Sciences, Retail, and Entertainment. We build custom generative AI and agentic AI systems on live client data, not off the shelf products and not demos. We are hiring a Senior Solution/Product Owner for our AI Delivery practice, a forward deployed seat at the intersection of frontier AI and enterprise transformation. You will own both the story and the delivery for client engagements built on Cowork Activation, Agentic SDLC with Claude Code, and Blueprint Activation. In one week you might frame an ROI case for a CFO, align a healthcare or financial services stakeholder on a compliant GenAI rollout, and pair with a Forward Deployed Engineer to wire an MCP integration or ship a Claude Code harness. What You Will Do: Own client engagements end to end, from use case framing and business case through scoped delivery, first deployment, and measurable results Define the value proposition, positioning, and reusable artifacts for your assigned Program or Blueprint, and align sales, delivery, and account teams around them Build business cases covering market fit, ROI, and competitive positioning, and translate them into scoped statements of work Run discovery, use case validation, design thinking workshops, and user research, and turn findings into demos and production deployments Stand up Cowork Activation, Agentic SDLC with Claude Code, or Blueprint Activation on live client data, driving rapid prototyping from use case to context to result Build hands on with the Provectus delivery stack, including Claude, Claude Code, MCP servers, connectors, Skills, and plugins Define product vision and roadmap for GenAI, LLM, RAG, and agentic AI engagements, and judge solution quality alongside architects and engineers Break complex work into incremental releases, and balance delivery speed against technical debt and architecture Lead Agile and Scrum execution across backlog, sprint planning, and release, following our Sprint, Enable, Realize engagement model Define success metrics and analytics, and drive continuous improvement through root cause analysis Contribute to the measurement framework behind our board level AI Maturity Index, ROI signal, and cost read Support change management, stakeholder alignment, enablement, and reusable playbooks so wins repeat across accounts What You Will Bring: 5 to 7+ years in product ownership, solution ownership, technical product management, or delivery leadership 3 to 5+ years working with AI/ML, generative AI, LLM applications, agentic systems, or data platforms Deep domain expertise in Financial Services and Insurance and/or Healthcare and Life Sciences, ideally in a client facing or consulting context Track record operating forward deployed or embedded with clients, as a Forward Deployed Engineer or Forward Deployed Expert, including hands on prototyping Proven ability to take an offering, program, or product from zero to one and land it as real client delivery Daily working use of Claude, Claude Code, and similar GenAI tools, including building your own skills, workflows, and productivity tooling Comfort with MCP (Model Context Protocol) integrations, connectors, and custom agent harnesses Understanding of GenAI and agentic patterns, including prompt engineering, RAG, embeddings, vector databases, and agent frameworks, deep enough to assess quality and translate it for executive stakeholders Data fundamentals: what ETL/ELT, data modeling, and data governance mean in an enterprise context Familiarity with AWS as a primary cloud platform, including Bedrock, and working awareness of MLOps and AI observability Strong Agile and Scrum experience, and familiarity with outcome based, milestone priced consulting engagements Design thinking, data driven decision making, and executive storytelling that holds up in front of a CFO or CIO Enough engineering empathy to earn credibility with senior engineers and solutions architects on day one Bias to action and structured problem solving in ambiguity Based in the San Francisco Bay Area or New York City metro area, and authorized to work in the US without sponsorship Nice To Have: A second priority vertical beyond your primary Financial Services or Healthcare focus Prior experience in an AWS Premier Partner or Anthropic partner environment Claude Certified Architect, Seller, or Developer certification, or willingness to complete one early Certifications such as CSPO, SAFe PO/PM, or AWS, Azure, or GCP Software development or solutions engineering background What We Offer: Base salary range of $140,000 to $180,000 USD depending on experience Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare The chance to shape how leading enterprises adopt AI, from strategy through first deployment A forward deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Health, dental, vision, and 401(k) with company match Unlimited PTO Access to the latest AI tools and premium subscriptions Internal training programs with full support for AWS and Claude certifications Hybrid work in San Francisco or New York

Location: London Workplace: OnSite Company Description We’re http://checkout.comCheckout.com http://Checkout.com. You might not know our name, but companies like eBay, Spotify, Klarna, Uber, and Sony do, because we’re behind many of the digital experiences you use every day. We are where the world checks out, enabling over 10 billion transactions yearly for more than one billion global shoppers. Whether you want to book a holiday, order food, renew a subscription, or check out online, there’s a good chance our tech powers the payments behind the scenes. Our platform helps the most ambitious businesses deliver effortless digital experiences, at scale. If you want to do career-defining work, you’ve come to the right place. We move fast, think globally, and believe great teams are built by hiring exceptional people with conviction, curiosity, and the desire to make an impact. With 20 offices across six continents and London as our HQ, we’re shaping the future of fintech – and we’re just getting started. As an ML (Machine Learning) Engineer at Checkout.com http://Checkout.com in the Disputes ML team, you will contribute to the development of our brand-new, ML-driven dispute optimisation suite. This is a unique opportunity to get in on the ground floor of an expanding area, grow alongside top-tier engineers, and make a tangible impact on millions of disputes. You will be building solutions that power our stack of value added services in the Disputes area. We’re a growing team in an expanding area within the company and we’re looking for individuals who have strong ownership, are passionate about productionising ML and have a pragmatic approach to converting big problems into smaller iterations to constantly deliver value. How you’ll make an impact - Build systems for training, deploying and monitoring machine learning models used in our Disputes platform, at scale - Build and optimize data pipelines and backend services to process dispute and payment data in real time - Build and scale our feature store for use-cases both online and offline - Take complete ownership of delivering comprehensive, end-to-end features within a startup-like setting, driving the entire lifecycle from requirement refinement, data pipeline construction and model training to troubleshooting and production deployment - Turn raw data into production-ready features that feed our dispute systems - Collaborate with platform and backend engineers to integrate models seamlessly Experience and qualifications - 5+ years of experience as MLOps /ML Engineer - High proficiency in writing clear, production-ready Python code - Experience with production ML models (online or offline) and standard MLOps practices - Experience with monitoring and observability of production systems, with a strong sense of ownership - Experience with training and operating models on Databricks - Familiarity in Cloud-based application development (we use AWS & Azure) - Familiarity with one or more ML frameworks and technologies: scikit-learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton - Strong communication skills, able to express ideas clearly and collaborate across teams Additional Information Bring all of you to work We create the conditions for high performers to thrive, through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity, and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here. It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable. Life at Checkout.com http://Checkout.com We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection. Curious about what it’s like to be part of our team? Visit our Careers Page https://www.checkout.com/careers to learn more about our culture, open roles, and what drives us. For a closer look at daily life at Checkout.com http://Checkout.com, follow us on LinkedIn https://www.linkedin.com/company/checkout/life/ and Instagram https://www.instagram.com/checkout_com/

Разрабатывать и исследовать ML-модели для генерации изображений и визуализации интерьерных/строительных решений на основе товаров Лемана ПРО. Проектировать и реализовывать end-to-end пайплайны для PoC: от сбора и подготовки данных до обучения, оценки и демонстрации результатов.

Location: Москва Команда центра аэрологистики, которая входит в состав департамента автономных решений, занимается научными исследованиями в области построения логистических маршрутов с применением отечественных моделей беспилотников и отдельных технологических решений. Научные достижения и технологические разработки в этом направлении открывают широкие перспективы для дальнейшего повышения конкурентоспособности отрасли. Обязанности * Разрабатывать CV-компоненты: детекцию, классификацию, сегментацию, трекинг, геометрическое зрение и, при необходимости, визуальную одометрию * разрабатывать продуктовый инференс на Python/C++ с интеграцией в ROS 2 или другую целевую архитектуру * экспортировать и валидировать модели в ONNX, TensorRT или аппаратно-специфичные runtime * оптимизировать инференс: FP16/INT8, квантизация, прунинг, дистиляция * профилировать полный путь кадра: получение изображения, декодирование, preprocessing, inference, postprocessing и публикация результата * оптимизировать работу с GPU/NPU, памятью, потоками, очередями кадров и zero-copy механизмами с учётом ограниченных ресурсов edge-платформы * участвовать в MLOps-процессах с использованием ClearML и DVC. Требования * Экспертный уровень в CV/ML: понимание классического CV, архитектур CNN/Transformer, современных детекторов и трекеров, обучения, переобучения, аугментации, потерь, дисбаланса классов и ошибок обобщения * умение не только запускать обучение, но и анализировать, почему модель ошибается на реальных данных * продвинутый C++: production-разработка пайплайна инференса, управление памятью и буферами, многопоточность, обработка видеопотоков, API-интеграции, профилирование и устранение утечек * продвинутый Python: PyTorch, обучение, обработка датасетов, оценка качества, воспроизводимые эксперименты, экспорт моделей и автоматизация тестов * понимание GIL, multiprocessing и нативных расширений Python, когда они действительно нужны * экспертный уровень в edge inference: ONNX, TensorRT или эквивалентный runtime, работа с GPU/NPU, точность после квантизации, совместимость операторов, профилирование latency и throughput, память и потребление ресурсов * рабочий уровень ROS 2: понимание DDS, QoS, executors, callback groups, lifecycle, composition, intra-process communication, TF2, rosbag и диагностики распределённого графа. **Будет плюсом:** * Опыт диагностики проблем камер, MIPI/CSI, GStreamer, аппаратного декодера, драйверов и взаимодействия с runtime * опыт оценки качества не только по метрикам модели, но и по поведению всей системы на реальных видеозаписях и испытаниях * опыт оптимизации программных и аппаратных компонентов инференса * опыт работы с zero-copy механизмами и очередями кадров в условиях ограниченных ресурсов * опыт построения baseline-решений, анализа ошибок и выбора стратегии оптимизации при ограничениях по вычислителю или latency * опыт проверки корректности метрик, отсутствия утечек данных, численной корректности inference и стабильности системы * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. Условия * Комфортный офис в Сколтехе * офисный формат работы * конкурентная компенсация (оклад и премии по результатам деятельности) * ДМС c первого рабочего дня, страхование от несчастных случаев и тяжелых заболеваний * материальная помощь и социальная поддержка, корпоративная пенсионная программа * дисконт-программы от компаний партнеров * бесплатный фитнес на территории работодателя * подписка Прайм с возможностью совместного использования на трёх близких * внешнее и внутреннее профессиональное обучение: семинары, тренинги, конференции, корпоративная библиотека.

Задачи: — Автоматизация торговых операций — Разработка моделей управления рисками на основе искусственного интеллекта — Анализ эффективности торговых операций Требования: — Опыт работы с машинным обучением — Знание SQL — Опыт работы с большими языковыми моделями (LLM) — Опыт работы с LangChain — Знание MLOps — Опыт обнаружения аномалий — Опыт финансового контроля — Владение Python — Опыт работы с OpenAI API Условия: — Гибридный формат работы в Лондоне

Location: 🇧🇷 São Paulo / 🌎Remote Workplace: Remote A little bit about us: We are Belvo, an open finance API platform with the bold vision of democratizing access to financial services in Latin America. We enable any financial innovator to access and interpret financial data, as well as initiate payments from their end-users accounts. We’re turning the messy complexities of the Latin American financial ecosystem into a modern set of tools to access and interpret data and move money in a seamless and secure way. We’re a highly-technical, passionate, and driven team. We are more than 90 people and our team currently represents 20 nationalities. We have offices in São Paulo and Mexico City – while a large portion of us work remotely. We are tackling a very stimulating problem: connecting fintech innovators with legacy financial infrastructure. We strive to go beyond the limits of what is possible today and to do so in an elegant and developer-first way. Since starting our adventure in May 2019, we have raised $71m from the leading VC investors globally. You can read more about our company here https://belvo.com/about/ and about our team and culture here https://belvo.com/careers/. Also, head to our blog https://belvo.com/blog/ for more news about what we’re building and how we work https://belvo.com/blog/category/working-belvo/. About the team: - We work in cross-functional, autonomous teams. We follow continuous delivery best practices executed on top of a modern technology stack.  - Our products are built for developers, by developers. Technological excellence is at the heart of what we do.   - We are pragmatic and customer-focused. We strive to find the right set of trade-offs in order to validate our hypothesis as early as possible, iterating on our products based on customer feedback.  - We communicate transparently. We do weekly all-hands where we get together to discuss company performance and goals. - While we are global and remote-friendly, we also operate from our vibrant offices in CDMX and Sao Paulo. To accommodate the various time zones in which we are based, we ensure we’re always synced up for at least 3 hours per day. - Also, we are backed by some of the leading investors in Silicon Valley and Latin America, including Founders Fund, Kaszek Ventures, and YCombinator. The challenge The Intelligence Squad is Belvo's AI enabler team. One part of the squad builds the platforms and patterns that let every team at Belvo ship AI. This role sits in the other part: turning the financial data and payments we aggregate across Latin America into models that make real decisions, who looks creditworthy, which transaction is fraudulent, what category a payment belongs to, whether an income statement checks out. That data drives real financial outcomes for the people and businesses on the other end, so the bar for rigor is high. You'll be the data science reference in the squad. We're not hiring someone to execute a modeling backlog someone else wrote. We want someone who decides which problems are worth modeling, defines how we know a model is good enough, and raises the data science bar for everyone they work with. You'll be deep enough in statistics and ML to defend a modeling choice to anyone, whether this is a set of rules to simplify a problem or a complex model that you build. You'll be close enough to the business to turn a commercial problem into a modeling roadmap and a business case. And you'll be a strong enough engineer to take your models from notebook to production and own them there. Responsibilities - Model development and selection: Lead the design of statistical and ML models (classification, regression, ranking, NLP or LLM-based) for problems like risk scoring, fraud detection and transaction categorization. Choose approaches on their merits and defend those choices with data, not habit. - AI-leveraged delivery: Use agentic coding and research tooling as a core part of how you work: exploring data, generating and testing feature hypotheses, scaffolding pipelines, writing the service around the model. We care about the decisions you ship, not hours at the keyboard, and we expect you to keep compressing the gap between the two. - Feature engineering with business context: Work with Product and the business teams to figure out which signals in Belvo's financial data actually explain the outcome you're modeling, and translate an ambiguous business question into a well-scoped modeling problem. - Productionization and ownership: Take models past the notebook. Build or shape the pipelines, APIs and services that run them reliably in production, and own them once they're live: monitoring, drift, retraining, and retirement when the evidence says so. - Experimentation: Design and run experiments (A/B tests and offline evaluation) to confirm that a model change actually moves the metric it's meant to move. - Cross-functional partnership: Work closely with Engineering, Data Research, Product, and the rest of the Intelligence Squad to ship end to end, and with stakeholders across the business to keep your models grounded in what actually matters to them. - Business and customer partnership: Work directly with stakeholders and, when needed, customers. Translate ambiguous business questions into well-scoped problems, and explain trade-offs and impact clearly to non-technical audiences, executives included. - Staying current: GenAI moves weekly. We expect you to be genuinely plugged in: model releases, papers, and tooling, to hold an opinion on what's real versus hype, and to bring the useful parts back to the squad as a demo, a doc, or a session for our company-wide AI Chapter. - Framing the problem: Decide what's worth modeling. Push back when a rule, a heuristic, or a better data pipeline solves the problem without a model, and be willing to kill or retire your own model when the evidence says so. This position may be for you if - 5+ years of experience as a Data Scientist, Applied Scientist, or in a similar hands-on modeling role. - You've owned models end to end in production, from framing the problem to monitoring it live, and you can point to the measurable business impact they had. - You have a solid grounding in statistics and can explain how your models work at that level, not just how to call them. - You've built and compared more than one type of model (for example tree-based methods, regression, deep learning) for the same problem and can justify why you picked the one you shipped. - You're comfortable in Python for data science (pandas, scikit-learn, and at least one of TensorFlow or PyTorch) and in SQL for pulling and shaping your own data. - You've taken several models you built into production yourself, not just handed it off to someone else to deploy.You http://deploy.You understand engineering fundamentals: testing, version control, CI/CD, and how to write code someone other than you can maintain. - You've built with LLMs past the API-call level: tool use or agents, retrieval, structured outputs, and shipped at least one to production. You can explain how you knew it was good enough, which means you've built some form of evaluation, not just eyeballed outputs. - Agentic coding tools are part of your daily workflow, not a novelty. You can walk us through a specific task that used to take you days and now takes hours: how you set it up, what you delegated, and where you still don't trust the output. - You're proficient in written and spoken English. Amazing if - You've worked in fintech, financial risk, or another regulated data environment before. - You have a business or strategy background alongside data science (an MBA, consulting, or a product/strategy role). - You've worked in a customer-facing data science role, such as a forward-deployed engineer or a solutions/applied team at a data platform company. - You've worked with MLOps tooling for model versioning, monitoring, and automated retraining. - You've deployed models on AWS (for example SageMaker or an equivalent managed ML service). - You've written evals or an LLM-as-judge harness for a non-deterministic system, and have opinions about where that approach breaks. - You've used LLMs inside the data pipeline itself: labeling, extraction, categorization, and can say where they beat a classical model and where they quietly don't. Tech stack - Python is our primary language for data science and modeling (pandas, scikit-learn, and deep learning frameworks as needed). - SQL against a cloud data warehouse for data access and feature work.Claude deployed company-wide and Claude Code for every engineer. Agentic tooling is part of how we work daily, not a side experiment. - Cloud infrastructure on AWS, favoring managed services so you can focus on the modeling problem rather than infrastructure plumbing. - An experimentation platform for A/B testing and offline evaluation. - We use Datadog for service observability across the company. - We follow Continuous Integration and Continuous Delivery best practices. Our tech stack: - We’re building our platform using modern technologies, putting the focus on reliability and long-term maintainability - We primarily use Python on the backend. We use battle-tested technologies such as Django, and we are heavy users of Python’s asyncio for some parts of our stack - We use Javascript, Vue.js and Sass on the frontend. We are developing and leveraging our own design system and component library - We run our infrastructure on top of Amazon Web Services, leveraging managed services. We tend to favor the use of managed services wherever possible, in order to focus on our business problems - We observe and monitor our services using Datadog - We follow Continuous Integration and Continuous Delivery best practices Our process steps: At Belvo every hire is so important to us, that we share the decision to hire as a team. - People team chat - Take-home challenge - Challenge presentation - Team fit chat - Meet the founders Our perks: 🚀 Stock options (we are all owners and this is very important to us) 📈 Annual company bonus linked to company performance 🔆 Flexible working hours 🔝 Remote friendly 🐶 Pet friendly 👩🏻‍⚕️ Health Insurance 🎊 Paid time off on your birthday 💻 Renew your laptop every 3 years 🙋 Training Budget 😎 Team building events 🙀 Bank holidays swap within 30 days 🔋 Fitness/ wellness stipends 🚀 Yearly company offsite 🍒 Fresh fruit every week, all-you-can-drink tea and coffee 🏝 Extra days off accrued when completing 2, 3, 4 years and beyond at Belvo 🎳 Yearly department offsite

Location: Москва Мы — крупная технологическая компания, работающая на переднем крае инноваций. Наша команда занимается созданием сложных, высоконагруженных и масштабируемых систем. Для усиления нашего подразделения мы ищем опытного специалиста, сочетающего глубокие технические знания с архитектурным мышлением и лидерскими качествами. Обязанности * Проектирование и проработка архитектуры решений с использованием современных паттернов проектирования. * Проведение архитектурного контроля за реализуемыми командами решениями. * Технологическое Research & Development (RnD), включая создание Proof of Concept (PoC) в областях **GPU, AI и MLOps**. * Проведение Code Review, лидирование процессов технологического обновления стека, внедрение мониторинга. * Участие в планировании задач: оценка рисков, сроков и трудозатрат. * Взаимодействие с бизнес-подразделениями: анализ потребностей, перевод требований в технические решения. * Разработка, поддержка и развитие референсных архитектур и стандартов для команд разработки. * Анализ, исследование и внедрение лучших практик (как внутренних, так и внешних). * Организация площадок для обмена опытом и совместного обсуждения архитектурных решений. * Тесное взаимодействие с командами корпоративной архитектуры и информационной безопасности для соблюдения стандартов и требований. Требования **Профессиональные навыки (Hard Skills):** * **Технологический стек:** Глубокий опыт FullStack-разработки с использованием **Java/Python, Spring Boot, React, PostgreSQL, SQL** * **Архитектура и инфраструктура:** Опыт проектирования, развертывания и поддержки приложений с использованием **Docker, Kubernetes (k8s), Kafka, Hadoop, GraphQL**. * **Архитектурные стандарты:** Строгое понимание и практическое применение стандартов **OpenAPI, AsyncAPI**. * **Системное проектирование:** Опыт создания наблюдаемых, отказоустойчивых и масштабируемых систем. **Личностные качества (Soft Skills):** * **Аргументированность и убедительность:** Умение ясно доносить сложные технические идеи, обосновывать архитектурные решения и «продавать» результаты своего труда команде и бизнесу. * **Стрессоустойчивость и ответственность:** Способность эффективно работать в условиях неопределенности и высоких нагрузок. * **Критическое мышление:** Глубокое, «инженерное» понимание того, как работают решения под капотом. Условия * График 5/2, офис на м. Кутузовская; * Режим работы - гибрид; * Достойный уровень вознаграждения (оклад + годовая премия); * Возможность возглавить AI-направление и влиять на цифровую трансформацию банка; * Широкие возможности для профессионального развития: корпоративный университет и множество онлайн-программ обучения. Корпоративное обучение за счёт компании; * Бесплатная подписка СберПрайм+, многочисленные скидки и бонусы от партнёров: СберМаркет, МегаМаркет, Самокат, Еаптека и др.

Разработкой AI агентов на основе современных генеративных сеток (GigaChat и OpenSource); Разработкой системы офлайн- и онлайн-метрик для оценки качества LLM-приложений;

Location: Paris ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE ROLE As a Research Engineer, GPU performance, you will sit at the heart of that spectrum: your low-level work directly determines how fast our frontier models can be trained and served. Working hand-in-hand with Research Scientists, you'll make every GPU count. Mistral AI is hiring an expert in pushing large language model's GPU utilization and performance to their limits.. In this role, you will own the performance of our models end-to-end: writing the low-level code that extracts the maximum capacity from high-end hardware, and rethinking model architectures so they run efficiently at scale. WHAT YOU WILL DO - Develop low-level code that takes full advantage of high-end GPUs and maxes out their capacity — from custom kernels to memory-layout and communication optimisations. - Rethink parts of the generative model architecture to make them more suitable for efficient inference, in collaboration with Research Scientists. - Integrate low-level efficient code into a high-level MLOps framework, so that optimisations benefit every training run and deployment rather than a single experiment. - Profile, benchmark and squeeze performance out of large-scale training and inference on thousands of GPUs. - Model LLM execution on various GPUs with high fidelity to assess hardware usage and build serving performance estimates. WHAT WE'RE LOOKING FOR - High technical competence for writing custom low-level kernels and pushing GPUs to their limits. - High expertise in the distributed computation infrastructure of current-generation GPU clusters. - Overall understanding of the field of generative AI; knowledge of, or interest in, fine-tuning and using language models for applications. - Strong software-design instincts: testing, code review, CI/CD. - Self-starter, low-ego, collaborative. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: US, San Diego, CA Workplace: OnSite Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further. ABOUT THE ROLE As an Autonomy Engineer focused in VLA Pre-training, you will work on all aspects of training capable policies. You'll pre-train base models on a diverse, multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks, shape data collection processes, and explore effective ways to generate and use synthetic data. This is primarily a deep learning role, so we're looking for experience solving real-world problems with modern neural networks. Robotics experience isn't strictly required, but if you're coming from outside the field, be prepared to get up to speed on a new domain quickly.   WHAT YOU'LL DO - Post-train policies via behavior cloning and RL; own the full loop from data to deployment. - Partner with the Data Collection team to drive collecting new data: specify what good data looks like, identify failure modes, ensure diversity and coverage. - Work closely with external partners to ensure steady supply of high-quality pretraining-scale data. - Run pre-/mid-/post-training on VLA stack; explore new modalities and architecture changes. - Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak‑supervision labelling, curate balanced datasets, and auto‑surface fresh failure cases into retraining. - Work with MLOps & Data Platform teams to scale distributed training and optimize models for real‑time edge inference. WHAT WE'RE LOOKING FOR - 3+ years building deep‑learning systems (industry or research) with shipped models or published artifacts to show for it. - Deep hands‑on experience with at least one of: LLMs, VLMs, or image/video generative models — architecture, training, and inference. - Experience with deep learning infrastructure: streaming datasets, checkpointing & state management, distributed training strategies. - Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code. - Familiarity with modern software engineering practices. - You document experiments clearly and communicate trade‑offs crisply. NICE TO HAVE - Robotics or autonomous driving experience. - Experience applying RL to LLMs or robotics. - Experience with VLA (vision-language-action) models. - Proven productization of deep nets (latency/throughput constraints, telemetry, on‑device optimization). - Publications at top-tier deep learning conferences or equivalent open‑source contributions. - Familiarity with OpenVLA, Physical Intelligence (π) models, or similar open source VLA frameworks. WHAT WE OFFER - Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources. - Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays. - 401(k) retirement plan with employer match. - Equity included–we believe builders should share in what they build. - Free daily catered lunch, snacks, and drinks in‑office. - Collaboration with top‑tier engineers, researchers, and product experts in AI and robotics. - Freedom to influence the product and own key initiatives. For this role in California, the expected base salary range is $180,000–$300,000 USD per year; your placement in that range depends on how your experience maps to our internal leveling.

Location: United Kingdom; Brazil; Ireland; Peru; Costa Rica; Latvia; Argentina; Romania; Cyprus; Portugal; Slovenia; South Africa; Czech Republic; Panama; Estonia; Canada; Hungary; Mexico; Poland; Spain; Bulgaria Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients. The AI Infrastructure team builds and operates the production systems that power intelligent agents at scale. This team sits at the foundation of the agent platform, ensuring that model inference, orchestration, and execution layers are reliable, observable, and performant under real-world load. Working closely with the Agent Systems team and broader infrastructure partners, this group owns the core primitives that enable agents to safely operate across internal systems. The environment is high-scale and high-stakes — systems serve millions of users and must meet strict reliability, latency, and correctness standards. This is a deeply production-oriented team. Engineers here combine strong systems thinking with applied ML infrastructure experience, building in Rust and operating services where performance and failure modes matter.   THE OPPORTUNITY - Design and build the infrastructure layer powering AI agent systems in production - Develop high-performance Rust services that handle model inference, orchestration, and execution - Architect scalable systems capable of supporting millions of users and high request throughput - Build reliable ML infrastructure and MLOps patterns for model deployment, evaluation, and monitoring - Define guardrails, observability, and failure handling for agent-driven workflows - Optimize latency, throughput, and cost across inference and orchestration layers - Partner closely with the Agent Systems team to translate experimental prototypes into hardened production systems - Contribute to foundational infrastructure decisions in a high-scale, high-impact environment   WHAT YOU BRING - 5+ years of experience building and operating high-scale production systems - Strong proficiency in Rust and systems-level programming - Deep understanding of distributed systems, reliability engineering, and performance optimization - Experience operating services serving millions of users or high-throughput workloads - Familiarity with ML infrastructure, model serving, or MLOps in production environments - Experience designing observability, monitoring, and failure recovery systems - Strong collaboration skills working across infrastructure and applied engineering teams - High ownership mindset in high-stakes production environment   NICE TO HAVES - Experience building infrastructure for agent-based or LLM-powered systems - Background in high-performance networking, async systems, or low-latency architectures - Experience with container orchestration and cloud-native infrastructure - Familiarity with evaluation frameworks and model performance monitoring at scale - Experience working in fast-moving 0→1 or platform-building teams Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice