Вакансии

Founding Engineer IT Вакансии Ищем Founding Engineer, который будет доводить продукты студии от согласованного скоупа до продакшена примерно за две недели.

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 Ищем сильного руководителя Data Science, который возглавит развитие ML- и AI-направления в команде персональных коммуникаций Сбера. Команда помогает клиентам получать релевантные предложения в нужный момент и через удобный канал: в приложении, рекламе, отделении, контактном центре и других точках взаимодействия. Мы строим рекомендательные модели, определяем лучшее следующее предложение и контакт, управляем частотой коммуникаций и постоянно проверяем гипотезы через A/B-тесты. В этой роли вы будете влиять на решения, которыми пользуется вся клиентская база России. В работе — более 100 банковских и небанковских продуктов, свыше 70 каналов и сценариев персонализации. Наша цель — делать коммуникации действительно полезными для клиента и одновременно приносить измеримый эффект бизнесу: повышать конверсию, увеличивать CLTV и ускорять запуск кампаний. Обязанности * Руководить командой Data Science: развивать экспертизу, выстраивать процессы, помогать сотрудникам расти и достигать результата. * Формировать и реализовывать ML/AI-стратегию трайба. * Развивать модели склонности и рекомендательные алгоритмы, которые выбирают лучшее предложение, канал и время для коммуникации с клиентом. * Улучшать механизмы выбора следующего контакта, частоты коммуникаций и A/B-тестирования. * Интегрировать LLM-решения в рекомендательные системы: использовать RAG и генерацию персонализированных текстов предложений. * Совместно с продуктом, бизнесом и технологическими командами запускать ML-решения в промышленную эксплуатацию. * Оценивать бизнес-эффект моделей и помогать превращать ML-разработки в понятный финансовый результат. Требования * От 10 лет опыта в Data Science. * Не менее 5 лет опыта разработки и внедрения рекомендательных, uplift моделей в крупных компаниях. * Опыт управления командой от 10 Data Scientists. * Умение развивать ML-компетенции на уровне всего подразделения. * Опыт запуска сложных ML-проектов в продакшен с подтвержденным бизнес-эффектом. * Практический опыт работы с LLM в рекомендациях: RAG, генерация текстов и персонализированных офферов. * Стратегическое мышление, самостоятельность и ориентацию на результат. * Умение договариваться со стейкхолдерами, объяснять сложные решения простым языком и находить баланс между качеством модели, скоростью запуска и задачами бизнеса. Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития   * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера

Location: Warsaw, Mazowieckie, Poland; Limassol, Cyprus; Sofia City, Bulgaria Workplace: hybrid Capital.com is building out its internal platform capabilities, and AI is a growing part of that. The role combines architectural ownership with hands-on delivery: you will design, build, deploy and operate the platform, including its infrastructure and DevOps. Reporting to the Director of Product Security, you will set up this function as its first member and make the early technical decisions that the future team will build on. As Platform Architect, you will own the architecture of the platform that more than 1,000 employees rely on, including their access to AI tools such as Anthropic Claude, OpenAI and future providers. Responsibilities: Build the AI platform architecture: the reference design for how LLM capabilities are accessed, governed, monitored, and extended across the organisation. Own every architectural decision for the AI platform, and document, justify, and stand behind each one. Evaluate and select new models, tools, and frameworks as the market moves. Deliver the platform as a service, so teams can move their agents into governed cloud infrastructure. Govern Anthropic (Claude API, Claude Code, Anthropic Enterprise), OpenAI, and future providers, covering API access controls, credential management, model version policy, cost allocation, and acceptable use standards. Own technical relationships with Anthropic and OpenAI at the enterprise level. Build cloud infrastructure for AI workloads on AWS and GCP: Bedrock, SageMaker, Vertex AI, Cloud Functions, and supporting services (S3, Cloud Storage, IAM, and monitoring on both platforms). Deploy and securely configure n8n, and build automation workflows connecting LLMs to Jira, Confluence, Slack, Google Workspace, HR systems, and proprietary data sources. Design and secure MCP servers and gateways connecting AI agents to internal services. Build Python integrations for AI-powered internal tooling: RAG pipelines, agent frameworks, prompt libraries, and internal AI assistants. Deliver hands-on: you write the code, build the workflows, and deploy the tooling. This isn't an advisory role. Build data privacy and security architecture for LLM usage in a regulated environment, including prompt classification, output controls, and audit trails aligned with FCA, CySEC, ASIC, and DORA. Build observability across the full stack: token consumption, cost per team, latency, error rates, and policy violations across cloud platforms and direct API providers. Build an AI adoption framework of standards, patterns, and onboarding materials that let teams across Technology use AI safely and consistently. Run internal enablement through workshops, documentation, and direct support for teams moving from ad-hoc AI use to governed, repeatable processes. Report AI platform status, risks, and roadmap to Technology leadership Requirements: 8+ years in engineering, with at least 2 years building LLM-based systems in production, not prototypes or POCs. Comfort operating as the first person in a function: you set your own priorities, unblock yourself, and deliver without a team around you. Production-grade Python: tooling that others depend on, not scripts you hand off. Deep, hands-on experience with the Anthropic and/or OpenAI APIs: function calling, tool use, streaming, RAG, prompt management, token optimisation, and model versioning. Hands-on experience with agentic platforms, MCP servers, and MCP gateways, including designing flows between AI agents and internal services. n8n: deploying, securely configuring, and maintaining multi-step automation workflows at organisation scale. Solid AWS for AI workloads: Bedrock, SageMaker, Lambda, S3, IAM, and CloudWatch, deployed and operated in production. Solid GCP for AI workloads: Vertex AI, Cloud Functions, Cloud Storage, IAM, and Cloud Monitoring. Hands-on DevOps capability to deploy and run what you build. Architectural thinking: end-to-end system design, clear trade-off documentation, and the ability to defend decisions to senior stakeholders. Security engineering for what you build: secrets and credential management, key rotation, and least-privilege IAM across cloud and SaaS. Data protection and auditability by design: classifying what data can reach which model and provider, controlling and logging prompts and outputs, and building audit trails that hold up to a regulator. Fluency in the LLM threat surface, including prompt injection, data exfiltration through model outputs, insecure tool and agent use, and model supply-chain risk, with working knowledge of a recognised framework such as the OWASP LLM Top 10 or NIST AI RMF. Nice to have: Fintech, brokerage, or financial services background. DORA or ISO 27001:2022 knowledge as it applies to AI system risk. Familiarity with the EU AI Act and other AI regulatory requirements, as well as AI-specific risk and governance standards such as NIST AI RMF or ISO/IEC 42001. LLM orchestration frameworks: LangChain, LlamaIndex, or similar. Hands-on experience with LLM guardrail and DLP tooling: Bedrock Guardrails, Vertex AI safety filters, or prompt-firewall and PII-redaction tools. Experience building internal developer platforms or AI centres of excellence. AWS Solutions Architect Professional, Google Professional ML Engineer, or equivalent. Grow with Capital.com and help shape how millions of people access the markets.

Mirage is looking for a Software Engineer Agents to build and deploy reliable agentic systems for creative video workflows.

- Сильный опыт в ML, Deep Learning, NLP и LLM. - Практический опыт создания AI-продуктов, работающих в production и в большом масштабе.

- Выстроить конвейер внедрения AI-решений: от заявки до поддержки; - Развивать Doc AI и автоматизацию обработки документов;

Location: Москва Команда управления развития искусственного интеллекта (блок «Развитие клиентского опыта B2C») ищет NLP/LLM-инженера для развития AI-copilot премиальных клиентских менеджеров. Мы переходим от классических NLP-решений к адаптивным мультиагентным системам, способным самостоятельно решать задачи в контуре бизнеса: поиск по базе знаний, суммаризация профиля, извлечение инсайтов из звонков, управление графиком, проактивные подсказки на звонке. Ищем Senior-инженера, который возьмет на себя архитектурную ответственность за решения с LLM под капотом, выстроит процессы оценки качества и будет писать чистый, поддерживаемый код, готовый к интеграции в продакшн выделенной бэкенд-командой. Обязанности * архитектура и разработка: проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы и автономных AI-агентов с использованием планирования, function calling и мультиагентной оркестрации * дообучение и адаптация: SFT/RLHF/DPO, LoRA/QLoRA под специфичные бизнес-задачи. Принимать решения о том, когда дообучение необходимо, а когда достаточно промпт-инжиниринга или RAG * система оценки (Eval): поддерживать культуру качества, внедряя продуктовые и ML-метрики (RAGAS, DeepEval, кастомные LLM-as-a-Judge), автоматизировать регрессионное тестирование, связывать технические метрики с бизнес метриками * качество кода и интеграция: писать чистый, хорошо документированный и тестируемый python-код, который легко интегрируется и поддерживается бэкенд командой. Формировать стандарты разработки в рамках DS экспериментов и проектировать API-контракты для ML-компонент * менторство и влияние: участвовать в код-ревью, формировать технические стандарты команды, декомпозировать бизнес-задачи и предлагать оптимальные ML-решения. Работать напрямую с продуктом и стейкхолдерами. Требования * опыт в DL/NLP от 4 лет включая обучение/дообучение моделей на PyTorch, из них не менее года практического опыта с LLM * практический опыт построения AI-агентов и LLM-пайплайнов с использованием LangChain/LangGraph * глубокое понимание архитектуры современных LLM, включая механизмы attention, MoE, RoPE, KV-cache, методы alignment (RLHF/DPO), принципы эффективного инференса * сильная инженерная культура: Python (asyncio, pydantic, fastapi), написание чистого тестируемого кода (pytest), опыт разработки асинхронных микросервисов. Понимание Docker/K8s, CI/CD для ML * оценка и мониторинг: уверенное владение инструментами LLMOps (Arize, LangSmith, W&B, Phoenix). Опыт создания кастомных eval-пайплайнов и понимания того, как метрики влияют на продукт * работа с данными: опыт предобработки сложных данных для подготовки датасетов, знание SQL * автономность и коммуникация: Умение переводить неопределённые бизнес-задачи в технический план, аргументированно отстаивать архитектурные решения, работать без микроменеджмента. будет плюсом: * опыт опромышливания AI-агентов и знание принципов LLMOps, включая CI/CD, оркестрацию и управление версиями промптов * опыт интеграции ML-решений в высоконагруженные системы * понимание безопасности LLM-приложений. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы после испытательного срока * ежегодный пересмотр зарплаты, 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.

Location: Москва Наша команда развивает интеллектуальные системы для ведущего банка —рекомендательные сервисы и AI-агенты. Наша цель — создавать решения, которые помогают клиенту решить его потребности с помощью продуктов и сервисов экосистемы Сбера, персонализируют клиентский опыт и оптимизируют бизнес-процессы. При этом минимизируя ненужный инфо-поток от экосистемы на клиента. Мы ищем специалиста, который объединит экспертизу в анализе данных, бизнес-аналитике и продуктовом анализе. Тебе предстоит погрузиться в клиентские пути, формировать и проверять гипотезы, быть драйвером инноваций, влияя на развитие ключевых продуктов банка. Обязанности ### Чем предстоит заниматься: * работать с бизнес-заказчиками: выявлять и уточнять бизнес-требования, переводить их в фичи дорожной карты развития продукта * вести продуктовую аналитику: формулировать и отслеживать метрики успеха, анализировать воронки, проводить A/B-тесты и оценивать влияние изменений на ключевые показатели * анализировать данные по клиентам и запускаемым кампаниям * проводить ресерч новых источников данных для улучшения качества рекомендательной системы * участвовать в разработке и развитии стратегии развития продуктов, определять приоритетные направлений для исследований * анализировать рынок и продукты с точки зрения клиентского опыта, формировать и проверять - продуктовые гипотезы, разрабатывать концепции и прототипы * проектировать MVP на основе гипотез * согласовывать бизнес-процессы: UML диаграммы, бизнес-схемы * взаимодействовать со смежными командами в качестве заказчика * разрабатывать методологии расчетов ключевых показателей по направлению и механизмов их внедрения. Требования ### Что для нас важно: * высшее образование: техническое или экономическое * опыт работы в качестве CJE, продуктового аналитика, системного аналитика от 2 лет * опыт работы с системами управления взаимодействием с клиентом и рассылками (CRM, CDP) * опыт организации исследовательских сессий, сбора и обработки обратной связи * опыт в продуктовых исследованиях (customer development, дизайн-мышление, ux-исследования и пр.) * знание основ статистики (методология оценки A/B-тестов, проверка гипотез) * знание BPMN и опыт построения процессов и постановки требований * умение ставить эксперименты и интерпретировать результаты * навыки продуктовой аналитики: определение метрик, A/B-тестирование, проверка гипотез, анализ воронок * навыки применения ИИ в профессиональной деятельности (например, Гигачат, Кандинский). ### Дополнительным преимуществом будет: * опыт работы с LLM: проектирование, внедрение или оптимизация чат-ботов/AI-агентов (LangChain, RAG, fine-tuning) * опыт работы с BigData-стеком (Hadoop, Spark, Hive), умение строить и оптимизировать пайплайны обработки больших данных * опыт работы с HighLoad-стеком (Java, Scala, Flink), понимание принципов проектирования высоконагруженных распределенных систем * опыт работы аналитиком от 2-х лет, опыт в экосистемных компаниях будет преимуществом (Ozon/Яндекс/Тинькофф/MTC/WB) * владение Miro, Figma. Условия ### Что мы предлагаем: * срочный трудовой договор (декретная ставка) * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы * корпоративный спортзал и зоны отдыха * уникальную систему обучения Сбера для профессионального и карьерного развития * программу адаптации и помощь руководителя на старте * расширенный ДМС и льготное страхование для семьи * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписку Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера * корпоративную пенсионную программу.

Location: Limassol, Cyprus We are looking for a Node.js Team Lead to lead an AI-native core engineering team and take ownership of our accounts, wallet and bonus engine. In this hands-on role, you will combine backend architecture, technical leadership and daily use of AI coding agents to build secure, scalable services. You will lead two developers and a QA engineer, establishing a development workflow that improves delivery speed while maintaining high standards for financial accuracy and security. Key Responsibilities: Establish and own the team’s AI-native development workflow, including coding agents, repository rules, AI-assisted code reviews and guardrails. Own the architecture and development of core services, including player accounts, wallets, bonuses, limits and KYC integrations. Lead and mentor two developers and a QA engineer, providing technical direction and supporting delivery. Perform final reviews of AI-generated code affecting financial transactions and security-sensitive functionality. Define and maintain API contracts used by the Payments, Games and Sports teams. Ensure reliable transaction processing and data consistency across high-volume financial operations. Track the impact of AI-assisted development through cycle time and defect rates and keep the team’s AI tooling and practices current. Required Experience: 6+ years of hands-on experience with Node.js and TypeScript, including 2+ years in a technical leadership role. Daily, hands-on use of AI coding agents, with experience introducing and rolling out an AI-assisted development workflow across a team. Strong practical experience with NestJS, PostgreSQL, Redis and message queues. Proven experience building high-load systems that process financial transactions reliably and maintain data consistency. Strong backend architecture, API design and code review skills, with particular attention to security and transaction integrity. Nice to Have: Experience building Model Context Protocol (MCP) servers, internal AI agents or LLM-powered features using tool calling or retrieval-augmented generation (RAG). Experience developing iGaming wallets or bonus engines. Experience with Kubernetes and AWS. By joining us, you can expect: A unique opportunity to build a career in a global, fast-growing company. An attractive remuneration package, based on qualifications and experience. Continuous learning and development opportunities. Company-wide events and team activities, fostering a fun and supportive work environment. Wolt monthly allowance. Snack Heaven! Our fully equipped kitchen offers free snacks, fruits, and beverages. Medical and wellness app, supporting both mental and physical well-being! Apply now and let’s achieve success together Apply at https://aloplay.bamboohr.com/careers/118?source=aWQ9MTg%3D to become a member of our amazing team! We appreciate all applicants' interest in our open position. Due to the large number of applications, only those who are being considered for the role will be contacted. All applications will be handled with the utmost confidentiality.

Исследовать и сравнивать подходы к решению прикладных задач: от классического ML до LLM-пайплайнов. Строить и валидировать модели: классификация, NER, matching, извлечение и нормализация данных, ranking, генерация.

О компании и продукте: Компания занимается разработкой и поддержкой масштабной транспортно-логистической платформы-маркетплейса xPlanet, реализованной в контуре крупнейшего российского производителя подвижного состава для рельсового транспорта, компании Трансмашхолдинг (ТМХ). Платформа запущена в 2019 году, с 2020 года происходит декомпозиция монолитной Java-архитектуры в более чем 20 микросервисов на Golang, завершённая в 2024 году. В последние два года компания структурно трансформировалась, сформировав единую команду разработки с приоритетом на современные технологии и качественный delivery. Задачи: — Проектировать и разрабатывать микросервисы на Go — Развивать существующие доменные сервисы: рефакторинг, оптимизация, ликвидация технического долга — Проектировать схемы данных и миграции PostgreSQL с нулевым downtime — Работать с асинхронным взаимодействием: топология Kafka, идемпотентность, гарантии доставки, обработка ошибок — Отвечать за надёжность сервисов: метрики, структурные логи, трейсы, профилирование, разбор инцидентов — Участвовать в декомпозиции задач и технических решениях команды — Проводить code review, менторить менее опытных коллег, участвовать в технических интервью Требования: — От 5 лет коммерческой backend-разработки, из них от 3 лет на Golang — Глубокое понимание Go: конкурентность (горутины, каналы, context), работа с памятью и GC, профилирование через pprof — PostgreSQL на уровне проектирования схем и оптимизации: планы запросов, индексы, блокировки, уровни изоляции, миграции на живой базе — Опыт проектирования и эксплуатации микросервисов: границы сервисов, версионирование API, идемпотентность, ретраи, таймауты, консистентность между сервисами — Опыт работы с брокерами сообщений (Kafka или аналоги) и паттернами применения — Знания Docker, Kubernetes, CI/CD пайплайнов — Тестирование: unit, интеграционные (testcontainers или аналог), мутационные тесты, понимание покрытия на разных уровнях — Умение довести задачу до продакшена самостоятельно: от уточнения требований до мониторинга после релиза Работа с ИИ-инструментами: — Ежедневное использование агентных ассистентов (Claude Code, Cursor, Continue.dev, Codex или аналоги) — Умение ставить задачи модели: сбор контекста, границы и критерии приёмки, разбивка задач на проверяемые шаги — Понимание, где ИИ ускоряет процесс, а где создаёт риски — Критическая проверка сгенерированного кода и ответственность за результат — Знание MCP-серверов, скиллов, RAG, подключение внешних инструментов к ассистенту Будет плюсом: — Опыт программирования на Kotlin (API gateway реализован на нём) — Навыки работы с OpenTelemetry и стеком наблюдаемости: Prometheus, Grafana, Tempo, Loki, Pyroscope — Опыт trunk-based development и работы с фича-флагами (Growthbook, Unleash и т.п.) — Опыт работы с gRPC и контрактами между сервисами — Опыт менторства, развития инженерной культуры, участия в найме — Опыт внедрения ИИ-инструментов на уровне команды Условия: — Оформление по ТК РФ — Гибридный формат работы, полностью удалённый по согласованию с руководителем — ДМС со стоматологией — Бонусы аккредитованной ИТ-компании — Корпоративный спорт (бег, хоккей) — Развитая инженерная культура: CI/CD, автотесты, observability, отсутствие ручных релизов по ночам — Возможность влиять на технические решения, а не просто закрывать тикеты

Location: San Francisco, CA or Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker AI Gateway is our answer to the complexity of taking AI agents from prototype to production. It’s a powerful, intelligent, and secure control point that eliminates the toil of model orchestration, tool management, observability, and governance—so developers can focus on building incredible AI agents, not gluing together infrastructure. The Gateway sits at the center of modern AI applications, offering: - A model and tool routing layer with built-in security and cost optimization - A familiar OpenAI-compatible interface and MCP server - Unified observability and policy enforcement - Auto-RAG, tool injection, session summarization, and more We’re just getting started—and we need exceptional engineers to help us build the backbone of the future of agent-based development. _______________________________________________________________________ WHAT YOU’LL DO - Design and implement core systems powering the AI Gateway, including the model router, MCP gateway, and control plane - Build infrastructure that supports dynamic model selection/routing, auto-failover, cost-based routing, and policy enforcement - Own critical capabilities such as secure credential storage, session summarization, caching, and rate limiting - Develop APIs for developers building with OpenAI-compatible interfaces and the Model Context Protocol - Build the underlying infrastructure to support evaluation, telemetry, replay, and backtesting for agents and LLM workflows - Lead architectural decisions and mentor engineers as the team scales - Performance tuning - caching, session summarization, and rate limiting that keep the gateway fast under real production load - Contribute to roadmap planning, technical strategy, and cross-functional alignment KEY PROBLEMS YOU’LL HELP SOLVE - Building a unified abstraction over fundamentally different model and tool providers without leaking their differences into the developer experience - Making credential and identity handling for tool access both airtight and fast - Giving developers real-time and historical visibility into what their agents are doing, with policy enforcement that can't be bypassed - Keeping latency low and reliability high as traffic scales from early adopters to production fleets - Take part in on-call rotation for your team, respond to incidents, debug production issues, and drive continuous improvement of system reliability _______________________________________________________________________ WHO YOU ARE Required Qualifications: - 6+ years of backend engineering experience with production-grade systems - Deep knowledge of distributed and highly scalable systems, cloud-native infrastructure, and API design - Experience building secure, high-throughput services (e.g., gateways, proxies, load balancers, policy engines) - Fluency in Go, and/or Rust (both preferred) - Familiarity with AI/ML platforms or model serving infrastructure - A strong product mindset—you're excited about building developer-facing tools - Ownership mentality with a bias for shipping, learning, and iterating - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience Bonus Qualifications: - Prior experience with OpenAI, Anthropic, or similar LLM APIs - Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI) - Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways - Understanding of OAuth2.1, secret management, and cloud IAM systems - Experience with Kubernetes, Docker, and microservices architecture _______________________________________________________________________ WHY JOIN US? - Work on what’s next: The agent era is just beginning. Help define the infrastructure stack for the next generation of software. - Own mission-critical infrastructure: Be part of a small team building the backbone of secure, intelligent agent applications. - Impact at scale: Your work will touch tens of thousands of developers building AI-first applications. - Engineering excellence: Join a world-class engineering team that values simplicity, code quality, and deep technical thinking. - Remote-first culture: Work from anywhere with a team that trusts and empowers you. Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________   COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. United States: $160,900 – $260,700 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: San Francisco, CA or Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker AI Gateway is our answer to the complexity of taking AI agents from prototype to production. It’s a powerful, intelligent, and secure control point that eliminates the toil of model orchestration, tool management, observability, and governance - so developers can focus on building incredible AI agents, not gluing together infrastructure. The Gateway sits at the center of modern AI applications, offering: - A model and tool routing layer with built-in security and cost optimization - A familiar OpenAI-compatible interface and MCP server - Unified observability and policy enforcement - Auto-RAG, tool injection, session summarization, and more We’re just getting started and we need exceptional engineers to help us build the backbone of the future of agent-based development. _______________________________________________________________________ WHAT YOU’LL DO - Set technical direction for core systems powering the AI Gateway 0 the model router, MCP gateway, and control plane - and make sure architectural decisions hold up as usage scales by 10x - Consistently identify gaps in our infrastructure, process, or tooling before they become blockers, and drive the fix from idea to production - Take a significant, hands-on role in defining, delivering, and implementing the Gateway's technical strategy, in close partnership with engineering leadership - Own the hardest, highest-ambiguity problems in the system: dynamic model selection, auto-failover, cost-based routing, and policy enforcement that has to work end-to-end - Act as the go-to expert for secure credential storage, session summarization, caching, and rate limiting - the capabilities everything else depends on - Build the infrastructure that lets us, and our customers, evaluate, replay, and backtest agent and LLM behavior with confidence - Mentor senior and mid-level engineers, and raise the technical bar for the team through code, design review, and example - Partner with product, design, and leadership to shape not just what the control plane UI does, but where the product goes next KEY PROBLEMS YOU’LL HELP SOLVE - Define the unified abstraction layer across diverse model and tool providers (OpenAI, Anthropic, Google, AWS Bedrock) that the rest of the org builds on - Architect secure, scalable identity and credential vaulting for tool and model access, trusted enough to run at enterprise scale - Build the infrastructure for real-time and historical analytics of AI agent behavior that leadership and customers both rely on - Guarantee that policy enforcement and logging work end-to-end - from prompt to tool to response - with no gaps an auditor (or attacker) could find - Set the bar for developer experience through intuitive APIs and first-class observability - Take part in on-call rotation for your team, respond to incidents, debug production issues, and drive continuous improvement of system reliability _______________________________________________________________________ WHO YOU ARE Required Qualifications - 8+ years of backend engineering experience, with a track record of owning production-grade systems at significant scale - Deep specialization in distributed systems, cloud-native infrastructure, and API design - recognized by peers and cross-functional partners as a go-to expert, not just experienced - Experience building secure, high-throughput services (gateways, proxies, load balancers, policy engines), and a history of making judgment calls other engineers rely on - Fluency in Go and/or Rust - A track record of identifying systemic gaps - in architecture, process, or tooling-— and driving the fix across team boundaries, not just within your own - A strong product mindset, with real experience shaping technical direction rather than only executing against it - Comfortable operating with wide latitude: given a problem and significant autonomy in how to solve it, including when and who to pull in - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience BONUS QUALIFICATIONS - Prior experience with OpenAI, Anthropic, or similar LLM APIs - Familiarity with RAG architectures, vector databases, or agent frameworks (e.g., LangChain, AutoGPT, CrewAI) - Experience with policy engines (e.g., OPA), observability frameworks (e.g., OpenTelemetry), or API gateways - Understanding of OAuth 2.1, secret management, and cloud IAM systems - Experience with Kubernetes, Docker, and microservices architecture - A track record of setting technical standards that other teams or engineers went on to adopt _______________________________________________________________________ WHY JOIN US? - Work on what’s next: The agent era is just beginning. Help define the infrastructure stack for the next generation of software. - Own mission-critical infrastructure: Be part of a small team building the backbone of secure, intelligent agent applications. - Impact at scale: Your work will touch tens of thousands of developers building AI-first applications. - Engineering excellence: Join a world-class engineering team that values simplicity, code quality, and deep technical thinking. - Remote-first culture: Work from anywhere with a team that trusts and empowers you. Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________   COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. United States: $170,350 – $275,550 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Сбор данных из внутренних сервисов, LLM Gateway, GitLab, Jira, логов AI-агентов и других источников. Проектирование и наполнение баз данных и аналитических витрин для оценки использования CodeGen-платформы.

Location: US - Headquarters - Maryland - Columbia Who is Tenable? Tenable® is the Exposure Management company. Over 40,000 organizations around the globe rely on Tenable to understand and reduce cyber risk. Our global employees support 65 percent of the Fortune 500, 50 percent of the Global 2000, and large government agencies. Come be part of our journey! What makes Tenable such a great place to work? Ask a member of our team and they’ll answer, “Our people!” We work together to build and innovate best-in-class cybersecurity solutions for our customers; all while creating a culture of belonging, respect, and excellence where we can be our best selves. When you’re part of our #OneTenable team, you can expect to partner with some of the most talented and passionate people in the industry, and have the support and resources you need to do work that truly matters. We deliver results that exceed expectations and we win together! Your Role: Tenable's InfoSec team is looking for a Security Engineer to join our Security Engineering & Architecture group. This role was built deliberately to span disciplines rather than specialize in one: you'll split your time between securing our cloud environments and supporting general security engineering disciplines across the business — covering cloud and non-cloud systems alike. If you're early-career but curious across the breadth of InfoSec rather than narrowly focused on one domain, this role is built for you. Your Opportunity: Support day-to-day cloud security operations — monitoring, triage, and remediation of findings across AWS, Azure, and/or GCP environments using CNAPP/CSPM tooling. Contribute to Security Architecture Reviews for new systems, integrations, and vendor onboarding — regardless of whether the system in question is cloud-hosted, on-prem, or SaaS. You'll work directly with the architects and engineers who own this process today, learning the methodology and eventually taking reviews from intake to sign-off. Help identify and document misconfigurations, excessive permissions, and insecure IaC patterns (Terraform/CloudFormation), and help track remediation through to closure. Participate in risk assessments and threat modeling exercises for new projects — applying the same core analysis skills whether the subject is a cloud workload, an internal application, or a third-party integration. Assist with IAM/RBAC reviews and least-privilege efforts; flag static credentials, over-broad roles, and gaps in audit logging wherever they appear in the environment. Build foundational familiarity with how AI/agentic systems intersect with our environment — cloud-hosted LLM services (AWS Bedrock, Azure OpenAI, GCP Vertex AI), the Model Context Protocol (MCP), and the basic risk surface of agent/tool-calling architectures and apply that awareness during reviews when these systems show up in scope. Work cross-functionally with engineering, GRC, and TPRM to keep architecture reviews and cloud findings moving — this role is a connective thread between teams, not a silo. What You'll Need: 2–3 years of experience in a security engineering, cloud security, IT security, or related technical role (internships and adjacent IT/ops experience count). Working knowledge of at least one major cloud provider (AWS, Azure, or GCP) — IAM fundamentals, networking basics, and common misconfiguration patterns. Basic understanding of security architecture review or threat modeling concepts (e.g., STRIDE, data flow diagrams) — or clear aptitude and interest in learning them. Familiarity with core security engineering practices: least-privilege access, audit logging, secrets management (no static credentials), and secure configuration baselines. Comfort reading documentation and asking good questions across unfamiliar systems — this role touches a lot of different architectures, so curiosity matters more than deep specialization in any one of them. Strong written communication — you'll be documenting review findings and cloud remediation guidance for technical and non-technical audiences alike. And Ideally: Exposure to IaC security scanning (Terraform/CloudFormation) or CNAPP/CSPM/CIEM platforms. Some hands-on exposure to a formal architecture/design review process, even informally (e.g., reviewing a peer's system design for security gaps). Basic familiarity with AI/LLM concepts — prompts, context windows, tool/function calling, RAG — and awareness of frameworks like the OWASP Top 10 for LLM Applications or the NIST AI RMF. A relevant certification (Security+, AWS/Azure/GCP security certs, or similar) or equivalent self-directed learning. Prior exposure to compliance frameworks (SOC 2, FedRAMP, ISO 27001) relevant to architecture review sign-off #LI-Hybrid #LI-MS1 This is the base pay range for this position. Compensation for the role will depend on a number of factors, including the candidate's qualifications, skills, competencies, location and experience, and may fall outside of the range shown. Employees are also eligible for variable compensation in addition to base pay (commission for sales roles, bonus for non-sales roles), depending on company and individual performance. Tenable also offers a variety of comprehensive and competitive benefits which include: medical, dental, vision, disability and life insurance; 401(k) retirement savings with company match; an employee stock purchase plan; an employee referral program; flexible spending accounts; an Employee Assistance Program (EAP); education assistance; parental leave; paid time off (PTO); company-paid holidays; health and wellness events; and community programs. US Pay Range $102,500 — $136,500 USD We’re committed to promoting Equal Employment Opportunity (EEO) at Tenable - through all equal employment opportunity laws and regulations at the international, federal, state and local levels.  If you need a reasonable accommodation due to a disability during the application or recruiting process, please contact Recruiting@Tenable.com for further assistance. Tenable Data Consent Statement Tenable is committed to protecting the privacy and security of your personal data. Depending on your location, one or more of the following notices may apply to you: General Applicant Privacy Notice California Applicant Privacy Notice EU/EEA/UK Applicant Privacy Notice

Location: Lisbon; Portugal Workplace: Hybrid ROLE HIGHLIGHTS: - Role: Forward Deployed AI Engineer - Department: Forward Deployed Engineering - Location: Portugal (Lisbon) - Reports to: VP, Forward Deployed Engineering - Working type: Hybrid (On-site at customer locations approx. 50% of time) - Must be eligible and able to attain relevant national and NATO security clearances. - Must be based in-country and native level speaker of the country you're deploying to. THE MISSION OF THE FORWARD DEPLOYED AI ENGINEER In this role, you'll be the hands-on AI builder defense and intelligence customers rely on. You’ll deploy ICEYE’s intelligence platform into secure (often air-gapped) environments and rapidly ship AI-powered workflows on top of it—everything from Docker-based deployments to RAG systems fusing SAR imagery with intelligence reports. This is a ship-code-daily role: turning raw satellite data into mission-critical tools analysts use to protect nations. WHO WE ARE ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth. ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system. ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future. Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific. DAY TO DAY RESPONSIBILITIES: PLATFORM DEPLOYMENT & INTEGRATION - Deploy ICEYE's intelligence platform into customer secure environments (on-premise, air-gapped networks, classified data centers). - Build and maintain containerized applications (Docker) and manage deployments in customer Kubernetes clusters. - Develop APIs and data pipelines connecting ICEYE systems to customer C4ISR infrastructure and intelligence tools. - Troubleshoot deployment issues in disconnected environments without external internet access. AI SOLUTION DEVELOPMENT & CUSTOMIZATION - Build Retrieval-Augmented Generation (RAG) systems that combine ICEYE SAR data with customer intelligence documents for context-aware analysis. - Develop AI agents and workflows using frameworks like LangChain, LangGraph, or similar tools to automate analytical tasks. - Implement prompt engineering strategies and conversational AI interfaces that enable analysts to query satellite data using natural language. - Create Model Context Protocol (MCP) integrations connecting AI systems with customer data sources. DATA FUSION & INTELLIGENCE WORKFLOWS - Build data fusion pipelines combining SAR imagery with additional intelligence sources (AIS, RF signals, optical imagery, SIGINT). - Develop automated workflows that correlate and cross-reference information across different data modalities. - Create lightweight tools, dashboards, and alerting systems tailored to customer operational needs. CUSTOMER ENGAGEMENT & RAPID PROTOTYPING - Work embedded with intelligence analysts, data scientists, and military operators to understand mission-critical problems. - Rapidly prototype AI-powered solutions (code first, polish later) that address immediate operational requirements. - Conduct hands-on training sessions on platform capabilities and AI workflow usage. - Gather customer feedback and translate it into technical requirements for ICEYE's product and engineering teams. WHAT WE ARE LOOKING FOR MUST HAVE SKILLS: - 3+ years hands-on software engineering building and deploying production systems. - Strong Python programming skills (required) plus one of: Go, TypeScript, or Rust. - Hands-on experience with LLMs, prompt engineering, and RAG systems. - Working knowledge of agent frameworks (LangChain, LangGraph, pydantic-ai) and vector databases. - Experience with Docker and deploying in secure / isolated / air-gapped environments. - Practical experience with APIs, data pipelines, and integrating systems in production, - Familiarity with Linux and basic Kubernetes operations. - Builder mindset: ship fast, iterate with feedback, and prioritize working solutions over perfect architecture. - Strong communication + customer engagement, able to work embedded with intelligence analysts / military operators. - Strong spoken and written English, able to explain technical concepts to non-technical stakeholders. - Willingness to travel 40–60% to customer sites within the region. NICE TO HAVE SKILLS: - Experience in defense, intelligence, or government operational environments. - Deploying to on-prem data centers and/or air-gapped networks. - Familiarity with geospatial data, satellite imagery, or remote sensing. - Experience with MCP integrations, multi-agent systems, or agentic workflows. - Deeper Kubernetes, IaC (Terraform/Ansible), and/or offline CI/CD. - Understanding of intelligence analysis workflows and defense/government requirements. - Experience with data fusion, multi-source intelligence integration, or GIS tools (ArcGIS, QGIS). - Background in solution engineering, technical consulting, or customer success. - Experience in defense tech (e.g., Palantir, Anduril, Scale AI, similar startups). APPLICATION PROCESS - Talent Acquisition Interview - Take home task - Hiring Manager Interview - Hiring Team Interview - Offer Decision WORKING AT ICEYE At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win. HOW WE WORK (OUR VALUES) Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way. - Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions. - See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others. - Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions. - Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift. - Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins. BENEFITS Our benefits are designed to support your health and wellbeing—at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process. OUR COMMITMENT TO DIVERSITY, EQUITY AND INCLUSION We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table. We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help. Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at iceye.com https://www.iceye.com

Location: Москва Вакансия для того, кто хочет строить инновации мирового уровня и видеть реальный след от своей работы. Команда создаёт ИИ-решение для анализа реализации стратегии Сбера. Это кейс на уровне топовых мировых исследований в области сценарного моделирования комплексных экономико-социальных систем и построения предиктивных алгоритмов. Ищем в команду ребят с сильным техническим и концептуальным мышлением и жаждой создавать новое. Обязанности ### Обязанности * сбор, анализ, формализация и согласование требований (функциональные, нефункциональные) * участие в проектировании и анализе архитектуры системы, интеграционных взаимодействий и клиентского пути * формализация требований к AI-функциональности: функциональные требования + требования к качеству ответов LLM и критерии приёмки GenAI фич * верификация и приёмка AI-функционала с учётом недетерминированности * описание требований к агентским сценариям и интеграциям * формализация требований к оценке качества (evals, golden set, процедуры регресса) в постановках для разработки * описание требований к RAG и корпоративной памяти: состав и структура источников, разметка, права доступа, качество данных * подготовка технических спецификаций для команд разработки, консультации в процессе реализации, приемка готового функционала * участие в проведении тестирования разработанного функционала и проведение демонстрации разработанного функционала для заказчика. Требования ### Требования * опыт работы с высоконагруженными системами на позиции системного аналитика от 5 лет * понимание GenAI технологий (LLM, RAG, агенты) и умение формализовать требования к качеству недетерминированных систем * Глубокое понимание микросервисной архитектуры, практический опыт работы с Kafka или другими брокерами сообщений * опыт проектирования баз данных и умение написать SQL запрос средней сложности * понимание методологий проектирования сложных информационных систем, опыт моделирования бизнес-процессов (UML, BPMN 2.0) и построения архитектурных моделей * опыт разработки и документирование API-контрактов (REST, SOAP), проектирование RestApi в Swagger OpenAPI * умение тестировать запросы с помощью инструментов Postman/Insomnia/Swagger * понимание и опыт работы с форматами данных: JSON, YAML, XML, HTML * понимание основ UI/UX. ### Будет плюсом * навык работы в PlantUML * навыки чтения Python и Java кода * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия ### Условия * гибридный формат работы из офиса в г. Москва * 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 * офис на Ленинском проспекте с зонами отдыха и спортзалом * льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний.