Вакансии

Location: European Union What you'll be solving A platform engineer opens their AI client of choice, connects it to CAST AI, and types: "Why did our spend jump 40% in eu-west last week?" Thirty seconds later they have an answer, pulled from live cluster state and cost data, with links back to the evidence. No dashboards. No YAML archaeology. Just a question and a correct, permission-checked answer. Your job is to make that interaction boring. Reliably, securely, at production scale. Behind it sits the hard part: an AI assistant that reasons over thousands of production Kubernetes clusters, a platform that safely exposes company infrastructure to external AI clients, and quality pipelines that tell us when the agent is wrong before a customer does. Get it right, and operators stop clicking through consoles entirely. Get it wrong once, and an agent gives a confidently wrong answer to someone managing production. CAST AI already runs Kubernetes infrastructure autonomously. Our automation makes thousands of cost-and-reliability decisions a day. Cast Pilot is the team building the intelligence layer on top of that: AI assistants for Kubernetes and the cloud itself . Our assistant already answers thousands of questions a month from hundreds of organizations. Now we're opening the platform up: a public, permission-aware MCP server so customers can plug CAST AI straight into their own AI clients and agents, deeper connectivity across all three major clouds (AWS, GCP, Azure), and a continuous push to make our agents smarter, better grounded, and more capable. This is greenfield AI platform work on top of battle-tested infrastructure. Most features ship in 1 to 4 weeks. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. Requirements Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices, not just make something work. You're a generalist who is as comfortable debugging a distributed system as designing an auth flow or tuning an agent's behavior. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Experience building or operating large-scale SaaS platforms in production: multi-tenant systems, high volumes, and the failure modes that only show up at scale. Solid understanding of Kubernetes. Our entire platform runs on it, and the assistants we build answer questions about it. You should be at home with autoscaling, scheduling, and networking concepts. You've personally driven a complex project end-to-end. Hands-on experience with AI coding agents and agentic development workflows. This is how we build, not just what we build. You've used observability tooling in production. CI/CD and DevOps practices experience. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. What will make you stand out You've built agentic systems that shipped : MCP servers or clients, agent gateways, tool-use orchestration, RAG pipelines, evaluation frameworks, and you can talk about what broke in production and how you found out. This is the single biggest differentiator for our team. Deep hands-on experience with one or more of AWS, GCP, or Azure, with real understanding of IAM, compute, networking, and their APIs, not just console-level usage. Experience with LLM application quality: evaluations, prompt engineering at scale, latency and cost optimization of agent loops. Knowledge of OAuth 2.0 / OIDC and multi-tenant authorization design. Deep knowledge of EKS, GKE, or AKS internals. Kubernetes or cloud-native OSS contributions. Responsibilities Design and build the systems behind our AI assistant: agent flows, context retrieval, and the data connections that let it reason about live Kubernetes infrastructure. Build and evolve our public, permission-aware MCP server : curated tools, identity, audit, and strict safety gates, so customers can use CAST AI from their own AI clients. Develop and maintain cloud connectivity across AWS, GCP, and Azure, and the data foundations our agents build on. Write production Go services that interact with cloud provider APIs, the Kubernetes control plane, and LLM APIs in real time. Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1 to 4 weeks). Debug novel problems: an agent giving a wrong answer, a cross-cloud permission edge case, a data freshness issue that only appears at scale. Build the quality harness: evals, telemetry, feedback loops, that let us ship agent improvements without regressions. Contribute to product direction and backlog. Shape what gets built next, not just how it's built. Participate in the team's on-call rotation. Tools We Use Daily Programming Languages: Go AI & Agents: MCP (servers and clients), agent gateways, LLM provider APIs, RAG, evaluation pipelines Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Workflows & APIs: Temporal, GCP Pub/Sub, gRPC, REST Observability & Product Analytics: Prometheus, Grafana, Loki, Tempo, PostHog CI/CD & GitOps: GitLab/GitHub CI with ArgoCD and Kargo What's in it for you? Competitive salary (€6,500 to €9,000 gross, depending on the level of experience) Work on genuinely new problems: production MCP platforms and AI assistants for Kubernetes and cloud infrastructure are not solved domains. You'll be defining them, not following a playbook. Equity options. Real users, fast: your work ships to hundreds of organizations in weeks, not quarters. Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development, including access to international conferences and courses. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Estonia; Greece; Latvia; Lithuania; Poland; Slovakia; Ukraine What you'll be solving A platform engineer opens their AI client of choice, connects it to CAST AI, and types: "Why did our spend jump 40% in eu-west last week?" Thirty seconds later they have an answer, pulled from live cluster state and cost data, with links back to the evidence. No dashboards. No YAML archaeology. Just a question and a correct, permission-checked answer. Your job is to make that interaction boring. Reliably, securely, at production scale. Behind it sits the hard part: an AI assistant that reasons over thousands of production Kubernetes clusters, a platform that safely exposes company infrastructure to external AI clients, and quality pipelines that tell us when the agent is wrong before a customer does. Get it right, and operators stop clicking through consoles entirely. Get it wrong once, and an agent gives a confidently wrong answer to someone managing production. CAST AI already runs Kubernetes infrastructure autonomously. Our automation makes thousands of cost-and-reliability decisions a day. Cast Pilot is the team building the intelligence layer on top of that: AI assistants for Kubernetes and the cloud itself . Our assistant already answers thousands of questions a month from hundreds of organizations. Now we're opening the platform up: a public, permission-aware MCP server so customers can plug CAST AI straight into their own AI clients and agents, deeper connectivity across all three major clouds (AWS, GCP, Azure), and a continuous push to make our agents smarter, better grounded, and more capable. This is greenfield AI platform work on top of battle-tested infrastructure. Most features ship in 1 to 4 weeks. This is a location-specific opportunity. We are currently accepting applications from candidates residing in the following European countries: Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, Cyprus, Malta, Finland, Sweden, Ireland, Moldova, North Macedonia, Montenegro, Slovenia, and Ukraine. Requirements Strong software engineering fundamentals: you design clean abstractions, write maintainable code, and can justify your architecture choices, not just make something work. You're a generalist who is as comfortable debugging a distributed system as designing an auth flow or tuning an agent's behavior. Production experience with Go is strongly preferred; candidates without Go should demonstrate strong systems programming skills in a comparable language. Experience building or operating large-scale SaaS platforms in production: multi-tenant systems, high volumes, and the failure modes that only show up at scale. Solid understanding of Kubernetes. Our entire platform runs on it, and the assistants we build answer questions about it. You should be at home with autoscaling, scheduling, and networking concepts. You've personally driven a complex project end-to-end. Hands-on experience with AI coding agents and agentic development workflows. This is how we build, not just what we build. You've used observability tooling in production. CI/CD and DevOps practices experience. Strong English skills, both verbal and written. Startup mindset: adaptable, proactive, and comfortable with ambiguity. What will make you stand out You've built agentic systems that shipped : MCP servers or clients, agent gateways, tool-use orchestration, RAG pipelines, evaluation frameworks, and you can talk about what broke in production and how you found out. This is the single biggest differentiator for our team. Deep hands-on experience with one or more of AWS, GCP, or Azure, with real understanding of IAM, compute, networking, and their APIs, not just console-level usage. Experience with LLM application quality: evaluations, prompt engineering at scale, latency and cost optimization of agent loops. Knowledge of OAuth 2.0 / OIDC and multi-tenant authorization design. Deep knowledge of EKS, GKE, or AKS internals. Kubernetes or cloud-native OSS contributions. Responsibilities Design and build the systems behind our AI assistant: agent flows, context retrieval, and the data connections that let it reason about live Kubernetes infrastructure. Build and evolve our public, permission-aware MCP server : curated tools, identity, audit, and strict safety gates, so customers can use CAST AI from their own AI clients. Develop and maintain cloud connectivity across AWS, GCP, and Azure, and the data foundations our agents build on. Write production Go services that interact with cloud provider APIs, the Kubernetes control plane, and LLM APIs in real time. Own features end-to-end: from design through implementation, testing, and production rollout (most projects ship in 1 to 4 weeks). Debug novel problems: an agent giving a wrong answer, a cross-cloud permission edge case, a data freshness issue that only appears at scale. Build the quality harness: evals, telemetry, feedback loops, that let us ship agent improvements without regressions. Contribute to product direction and backlog. Shape what gets built next, not just how it's built. Participate in the team's on-call rotation. Tools We Use Daily Programming Languages: Go AI & Agents: MCP (servers and clients), agent gateways, LLM provider APIs, RAG, evaluation pipelines Cloud & Orchestration: Kubernetes, AWS, GCP, Azure Infrastructure as Code: Terraform Databases & Storage: PostgreSQL, Cloud Object Storage, ClickHouse Workflows & APIs: Temporal, GCP Pub/Sub, gRPC, REST Observability & Product Analytics: Prometheus, Grafana, Loki, Tempo, PostHog CI/CD & GitOps: GitLab/GitHub CI with ArgoCD and Kargo What's in it for you? Competitive salary (€6,500 to €9,000 gross, depending on the level of experience) Work on genuinely new problems: production MCP platforms and AI assistants for Kubernetes and cloud infrastructure are not solved domains. You'll be defining them, not following a playbook. Equity options. Real users, fast: your work ships to hundreds of organizations in weeks, not quarters. Enjoy a flexible, remote-first global environment. Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology. Spend 10% of your work time on personal projects or self-improvement. Learning budget for professional and personal development, including access to international conferences and courses. Annual hackathon to spark new ideas and strengthen team bonds. Team-building budget and company events to connect with your colleagues. Equipment budget to ensure you have everything you need. Extra days off to help maintain a healthy work-life balance. Hiring process Screening call with Recruiter Hiring Manager interview Technical interview (system design) Live coding Culture Check interview with an executive As part of our standard hiring process, a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. Please note that Cast AI does not provide any form of visa sponsorship/work permit. #LI-Remote

Location: Москва Отдел занимается разработкой AI-агентов для автоматизации бизнес-процессов. В фокусе — задачи NLP и CV: работа с текстовыми и визуальными документами, извлечение информации, принятие решений на основе содержимого. Требуется усилить отдел ML-экспертизой в области языковых моделей, промпт-инжиниринга и качества агентных решений. На этой позиции нужен Data Scientist, который отвечает за смысл и качество решений агента и обработки документов. Это возможность участвовать в создании агента enterprise-уровня с высокими требованиями к качеству и стабильности. Обязанности * развивать промпты агентов: системные и ролевые промпты, few-shot, схемы рассуждения, tool calling, извлечение структурированных знаний из документов * строить пайплайны эволюции промптов: версионирование, оффлайн-оценка (golden set, side-by-side, LLM-as-judge), итерации по ошибкам * решать задачи классификации и маршрутизации документов: разделение входного пакета по классам, подготовка релевантного контекста для агента * работать с RAG и эмбеддингами: chunking, выбор и тюнинг эмбеддингов, retrieval / rerank, оценка качества поиска * работать с open-source LLM и GigaChat во внутреннем контуре; предлагать и обосновывать стек под задачу Требования * опыт в Data Science / NLP / LLM от 3 лет * опыт prompt engineering на продакшн-задачах * понимание tool calling / function calling * понимание RAG-архитектур * опыт классификации текстов и документов * уверенный Python (эксперименты, скрипты, оценка качества) * понимание метрик качества LLM и подходов к оценке (eval-наборы, precision / recall по кейсам) **Будет преимуществом** * богатый опыт работы с эмбеддингами: выбор модели, fine-tune, contrastive learning, оценка retrieval * опыт пайплайнов эволюции промптов (DSPy, GePA и аналоги, Langfuse / PromptLayer) * опыт LangChain / LangGraph / LlamaIndex на уровне логики агента * опыт в доменах: банки, комплаенс, документооборот, юридические тексты * опыт LoRA / SFT * опыт CV / мультимодальных моделей Условия * комфортный современный офис рядом с м. Кутузовская * формат работы – офис/гибрид * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва Наша NLP команда занимается задачами, связанными с извлечением информации из документов, диалогов, текстов различной природы. Главной задачей для нас является создание готовых продуктов с LLM под капотом, а также создание self-service для бизнеса, где каждый может создать нужный ему сценарий (скилл) в режиме No-code. Обязанности * проектировать и калибровать judge-пайплайны: рубрики, эталонная разметка, согласованность с людьми, борьба с позиционным bias и bias по длине * строить RAG-поиск для дублей и противоречий: чанкинг, гибридный поиск, реранкинг, проверка противоречий на уровне утверждений * делать регресс-тесты знаний: golden set, пороги, прогон на каждое изменение промпта или модели * ловить галлюцинации: извлечение утверждений, привязка к источнику, верификация * дообучать и дистиллировать judge-модели, чтобы снизить цену и задержку без потери качества * собирать мультиагентные пайплайны с guardrails и контролем стоимости прогона. Требования * высшее образование: математика, физика, информатика или профильный data science * 5+ лет в ML, из них не меньше полутора лет с LLM в проде * RAG в продакшене, а не демо: с метриками качества, нагрузкой и историей улучшений * Fine-tuning: SFT, LoRA/QLoRA, желательно DPO. И понимание, когда дообучение не нужно * опыт построения и калибровки LLM-as-judge. Знаете, как judge врёт и как это ловить * Guardrails и детекция галлюцинаций * мультиагентные пайплайны: оркестрация, обработка отказов, ограничение стоимости * Python: пишете код, который уходит в прод. Тесты, ревью, код-стайл * английский для чтения статей и документации * специфика банка: много контрагетов, с ними нужно иногда коммуницировать. **Будет плюсом** * опыт python-бэкендера: FastAPI, асинхронность, очереди, PostgreSQL, векторные хранилища * графы знаний и онтологии * NLI, извлечение фактов, работа с разметчиками * публикации, доклады, open source. Условия * гибридный формат работы * годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * регулярные митапы и развитое DS-community * 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 Сбер.

Location: Москва Наша NLP команда занимается задачами, связанными с извлечением информации из документов, диалогов, текстов различной природы. Главной задачей для нас является создание готовых продуктов с LLM под капотом, а также создание self-service для бизнеса, где каждый может создать нужный ему сценарий (скилл) в режиме No-code. Обязанности * проектировать контракты API сервисов оценки: методы, схемы, коды ошибок, версионирование, идемпотентность * описывать пайплайны: приём документа → нарезка → вызов судьи → агрегация оценок → отчёт * собирать требования у владельцев баз знаний и превращать их в измеримые критерии * рисовать диаграммы: контекст, последовательности, состояния, данные * разбирать инциденты и расхождения оценок, ставить задачи разработке, принимать результат * взаимодействовать с заказчиками в рамках ТЗ. Требования * понимать и уметь объяснить, зачем нужно и когда не нужно: API, Swagger/OpenAPI, Kafka, JSON, ETL, Sowa, Redis, SQL, Postman, CI/CD * UML: последовательности, компоненты, состояния, ER * описание API на уровне спецификации, а не списка полей * опыт работы с командами разработки: декомпозиция, ревью реализации, приёмка * от 2х лет в роли системного аналитика на интеграционных проектах. ### Будет плюсом * опыт работы руками: настроить межсетевое взаимодействие через sowa, проверить API в Postman, написать SQL-запрос, достать значение из key-value хранилища * описание бизнес-процессов, BPMN * опыт с LLM: промпты, RAG, эмбеддинги, векторные БД, оценка качества ответов * python на уровне чтения кода и написания скриптов * опыт в Data governance или автоматизации контроля качества данных: RBAC/ABAC, data quality validation, управление консистентностью, документирование датасетов, policy-as-code. Условия * гибридный формат работы * годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * регулярные митапы и развитое DS-community * 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 Сбер.

Location: Москва Наша команда (70+ человек) создает технологический фундамент для внедрения GenAI в экосистему банка. Мы разрабатываем RAG-платформу, фреймворк исполнения агентов и системы обработки неструктурированного контента (документы, коммуникации). Наши сервисы оборачивают базовые LLM в готовые интерфейсы, позволяя бизнесу быстро создавать собственных агентов и GenAI-приложения. Мы обеспечиваем полный цикл разработки (E2E): ML-инженеры и бэкенд-разработчики работают вместе над реализацией сервисов, в связке с фронтендом и дизайнерами. У нас есть собственная команда разметки для оценки качества и сбора датасетов, с помощью которых мы дообучаем доменные модели под банковские сценарии. Масштаб задач: Сейчас в портфеле более 200 реализованных сценариев. В планах — реализация еще 300+ сценариев, включая создание слоя процедурной памяти для агентов, специализированных поисковых агентов и систем автоматической оценки качества GenAI (Auto-Eval). Развитие Self-Service инструментов (песочниц с UI), где команды банка могут тестировать гипотезы перед интеграцией. Чем предстоит заниматься Мы ищем инженеров, которым интересно не только R&D, но и создание полноценных продуктовых систем, способных обучаться на пользовательском фидбэке и решать конкретные бизнес-задачи. Обязанности * развитие GenAI сервисов: Создание и улучшение RAG-систем (в т.ч. Agentic RAG), инструментов Agent Observability, сервисов обработки документов * обучение моделей: тренировка и дообучение мультимодальных моделей на русском языке (OCR, Embeddings, Rerankers, VLM) * MLOps и Data Management: организация и автоматизация процесса разметки данных (от сбора до контроля качества краудсорсинга) * развитие автономных пайплайнов улучшения качества моделей с использованием LLM-as-a-Judge * Engineering: релиз новых моделей и сервисов в продуктовые среды. Требования * глубокое понимание LLM: опыт работы с большими языковыми моделями, Prompt Engineering, Fine-tuning LLM/VLM * инженерная культура: уверенное владение Python 3, знание ООП, принципов SOLID. Хорошее знание алгоритмов и структур данных. * LLM Stack: опыт работы с LangChain, LlamaIndex или другими фреймворками для разработки агентов и мультиагентных систем (MAS) * Vibe Coding: умение автоматизировать собственные рутинные процессы с помощью AI-инструментов * Mindset: желание постоянно изучать новые SOTA-подходы, модели и технологии * опыт руководства командой ML-инженеров. **Будет плюсом** * активный профиль на GitHub с качественным кодом * достижения (медали) на Kaggle * участие в Open-Source проектах, связанных с LLM/NLP. Условия * комфортный современный офис - м. Кутузовская * гибридный формат работы * ежегодный пересмотр зарплаты, годовой бонус * 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 * корпоративная пенсионная программа.

Location: Москва Мы создаем с нуля экосистему интеллектуальных агентов для автоматизации бизнес-процессов. Нам нужен универсальный специалист, способный вести фичи от проектирования базы данных до реализации сложного интерактивного интерфейса. Наша цель - разработка готовых решений «под ключ». Вы будете работать на стыке бэкенда и машинного обучения, напрямую влияя на то, как тысячи сотрудников банка взаимодействуют с новыми инструментами оптимизации. Обязанности * разработка backend для AI-агентов * интеграция с API существующих АС * реализация RAG-пайплайнов * оптимизация производительности * Code review Требования * 3+ года в backend-разработке * Python (FastAPI, Django) / Java / Go * опыт работы с LLM-фреймворками (LangChain, LlamaIndex) * PostgreSQL, Redis, MongoDB * Микросервисы, event-driven архитектура) Будет плюсом: * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. * навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом Условия * комфортный современный офис рядом с м. Кутузовская * 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 (для Junior позиций) * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Intellias Meet your recruiter Stephen Palmer stephen.palmer@intellias.com https://www.linkedin.com... Vacancy details Management Manager Director Bulgaria, Croatia, Poland, Portugal, Spain, United Kingdom Remote APPLY NOW REFER A FRIEND Over 20 years of market experience, Intellias brings together technologists, creators and innovators in Europe, North and Latin America, and the Middle East. Join our international team and take the mission to solve the advanced tech challenges of tomorrow! What project we have for you We are building a market-leading AI Solutions practice and are looking for a senior leader to anchor it across Europe. This is a hands-on leadership role at the intersection of technical depth, client advisory, and commercial growth. You will work with business segments and co-own AI outcomes end to end — from shaping opportunities and advising client executives, through architecting and delivering production-grade solutions, to building the team and offerings that let us scale. This is not a pure management role and not a pure architect role. The right person can sit with a client CTO in the morning to frame a transformation roadmap, dive into a model architecture or agent design review in the afternoon, and shape the next quarter’s offering and pipeline in between. You will be a founding pillar of how we take AI from pilot to production at enterprise scale. What you will do Client Advisory & Pre-Sales Act as the senior AI advisor and trusted partner to enterprise clients, translating business problems into pragmatic, value-driven AI strategies and roadmaps. Lead pre-sales and solutioning: shape opportunities, scope and estimate engagements, run discovery and design workshops, and present to senior and C-level stakeholders. Drive proposal and RFP responses, solution narratives, and commercial framing that differentiate us in competitive situations. Identify and grow account opportunities, partnering with business development to convert advisory engagements into delivery programs. Offering & Practice Development Define and productize repeatable AI offerings, accelerators, and reusable assets that shorten time-to-value and improve delivery margins. Establish reference architectures, delivery playbooks, and engineering standards for ML, GenAI, and agentic solutions. Contribute to AI practice strategy and external thought leadership (points of view, frameworks, speaking, content). Delivery Leadership Support end-to-end delivery of AI programs — from PoC and pilot through to hardened production — ensuring solutions actually ship and operate, not just demonstrate. Drive production readiness across the lifecycle: MLOps/LLMOps, evaluation, monitoring, cost management, security, and AI governance. Manage program economics, scope, risk, quality, and stakeholder alignment across distributed delivery teams. Technical Leadership Provide hands-on technical direction across machine learning, deep learning, and GenAI/agentic systems, with the credibility to lead architecture and design reviews. Make and defend platform and architecture decisions across hyperscaler and data ecosystems (AWS, Azure, GCP, Databricks, Salesforce etc.). Set technical standards and uphold engineering quality, mentoring senior technical staff. Team Building & Leadership Recruit, build, and grow a high-performing AI delivery organization in Europe across data science, ML/AI engineering, and solution architecture. Manage, coach, and develop senior practitioners; foster a culture of capability growth, certification, and continuous learning. Operate effectively within a collaborative leadership model alongside business development and technology peers. What you need for this 12+ years in technology, data, or engineering, with strong hands-on background in AI / ML / data science, including genuine hands-on practice (not solely oversight). Demonstrable hands-on expertise in machine learning and deep learning— model development and experimentation with frameworks such as PyTorch or TensorFlow, and a working command of modern ML/DL techniques. Proven track record delivering AI/ML solutions into production at enterprise scale — you have shipped systems that operate reliably in the real world. Production experience on at least one major hyperscaler or data platform(AWS, Azure, GCP, or Databricks); multi-platform fluency is a strong advantage. Hands-on production experience with GenAI and agentic systems — LLMs, RAG, orchestration and agent frameworks, tool/function calling, and evaluation of non-deterministic systems. Experience leading both internally and externally — line-managing internal teams and owning external client programs as a delivery lead, engagement lead, or equivalent accountable role. Executive-grade client presence — able to advise, influence, and build credibility with senior business and technology stakeholders. Nice to Have: Prior consulting or IT/professional-services experience — pre-sales, offering development, and accountability for running AI projects and programs (including revenue or margin contribution). Experience building or scaling an AI / data practice or capability from an early stage. Deep domain expertise in one or more priority verticals — e.g., retail, automotive & mobility, financial services, healthcare / medtech & life sciences, or telecom — sufficient to speak the client’s language and frame value, not just technology. Familiarity with EU AI Act, GDPR, and responsible-AI / governance frameworks as applied to enterprise deployments. Relevant certifications (AWS / Azure / GCP ML or AI, Databricks). Advanced degree (MSc or PhD) in Computer Science, Data Science, Machine Learning, Engineering, or a related field. Experience operating within nearshore / distributed delivery models. Additional European languages. What Success Looks Like (First 6–12 Months) Established credibility with key clients and internal stakeholders as the go-to senior AI leader for one or more business segments. Shaped and helped win AI engagements, with a measurable contribution to pipeline and bookings. Stood up or sharpened a set of productized AI offerings and accelerators that the team can sell and deliver repeatably. Promoted delivery and engineering standards and begun building a growing, high-quality AI team with measurable impact Delivered (or put firmly on track) at least one flagship AI program running in client production. What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Location: Los Angeles, California, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Forward-Deployed AI Engineer — Enterprise AI Location: Los Angeles, CA Experience Level: 8+ years Compensation: $200,000–$250,000 base salary + equity Work Model: In-office, typically three to five days per week, depending on client requirements Why Join Enterprise AI? Enterprise AI is a distinct business unit within Turing, operating with the speed, focus, and ownership of a rapidly scaling startup. When it comes to applied AI in the workplace, you’ll define the frontier by building beyond it. You’ll join the initial team of Forward-Deployed Engineers, shaping how we work while solving enterprise-grade problems for the world’s leading enterprises. As a Forward-Deployed AI Engineer, you’ll own outcomes end to end—from identifying high-value customer problems and shaping architecture to building production systems and working directly with senior client leaders. You’ll develop generative AI solutions in the real world, driving measurable impact and return on investment within large institutions. Enterprise AI is intense, intellectually challenging work. It won’t be for everyone, but it may be exactly the challenge you’re looking for. As a Forward-Deployed Engineer, You Will Find the Problem Worth Solving Work directly with customers to navigate ambiguity, uncover high-value opportunities, challenge assumptions, and translate complex business needs into clear technical direction. This requires curiosity and business acumen as you discover business workflows and jointly define the outcomes an AI solution should drive. Build Beyond the Frontier Architect and build production AI systems, from multi-agent workflows, orchestration, tool use, context engineering, memory and state, and retrieval-augmented generation (RAG) to the APIs, data pipelines, integrations, and distributed systems that make them reliable at scale. Own the Outcome You won’t simply be given a list of responsibilities—you’ll have the authority to own the outcome for your client and make a real-world impact through your work. You’ll lead solutions from discovery through deployment and measurable impact while staying hands-on and owning evaluation, observability, security, reliability, latency, scalability, and cost. The Anti-Sell High ownership and intensity: This role requires exceptional ownership and accountability. You’ll work with clients who have uncompromising expectations for quality, making this a demanding environment—and an ideal proving ground for people who seek difficult, high-impact challenges. Client-first and in-office: Our work is client-first, which means in-person collaboration may be required either at the client site. Depending on your assignment and client needs, you should expect to work in person three to five days per week and be based in the Los Angeles Metro area or within a reasonable commuting distance. You’ll help write the playbook: Enterprise AI operates like a startup. You may not always have an established process or playbook because you’ll be expected to apply your expertise, judgment, and technical depth to create one. Our Forward-Deployed Engineers Typically Have Deep production engineering experience: Typically 8+ years building and operating sophisticated software systems, with expertise in Python, backend systems, APIs, databases, distributed systems, system design, and cloud infrastructure. Hands-on agentic AI experience: Experience building production LLM and agent systems using orchestration, tool use, context engineering, memory and state, retrieval, and multistep workflows. End-to-end systems ownership: A track record of taking complex systems from architecture through deployment and scale, with a practical understanding of evaluation, observability, reliability, security, guardrails, latency, scalability, and cost. Exceptional customer and technical leadership: The ability to enter unfamiliar environments, earn trust quickly, navigate ambiguity, challenge assumptions constructively, and communicate effectively with senior engineers and business executives. Client-ready communication: The ability to explain technical concepts clearly, navigate ambiguity, work with stakeholders, and remain calm under pressure. Don’t Meet Every Qualification Yet? If you don’t meet every qualification for this role, we encourage you to apply to our Forward-Deployed AI Engineer (In Training) opportunity. This role is designed for high-potential software engineers and full-stack developers who are building the production engineering, applied AI, and client-delivery skills required to become Forward-Deployed AI Engineers at Turing. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Intellias Meet your recruiter Olha Yakuba olha.yakuba@intellias.com https://www.linkedin.com... Vacancy details Data Engineering Data Architect Architect Bulgaria, Croatia, Poland, Portugal, Spain, Ukraine, United Kingdom Remote APPLY NOW REFER A FRIEND Let’s breathe life into great tech ideas! With 3,000 people globally, Intellias is a company where benchmark technological solutions are born. Join in and take your part in digitalizing the world. What project we have for you The client is a US-headquartered engineering, architecture, construction and commissioning firm delivering complex facilities for the life sciences and advanced technology sectors. Its knowledge lives across SharePoint, CRM, project management, cost and document systems, and the client is investing in data and AI to put that knowledge to work. Intellias is the client’s data and AI delivery partner. The engagement covers several streams that run in parallel or follow one another: Data platform: a governed Microsoft Fabric lakehouse that consolidates project, commercial and document data into one trusted source. Knowledge and document AI: extraction, search and cited generation over large archives of proposals, specifications, drawings and project records. AI agents and workflow automation: assistants embedded in pre-construction, proposal, delivery and innovation processes, including idea intake, triage and approval flows. Governance and enablement: AI governance, security, environment set-up, including isolated or offline deployments, and capability building for client teams. What you will do Own the account’s target data and AI architecture, keep every initiative aligned to it, and maintain the architecture documents and decision records. Design lakehouse data models (medallion, Delta) with clear lineage, data quality rules and reusable data products. Plan environments, capacity and the path from pilot to production. Design integrations with enterprise systems such as Microsoft 365/SharePoint, CRM, ERP, finance and project systems, plus entity resolution to link clients, projects and documents. Build document intelligence pipelines that use OCR, classification and LLM-based extraction. Architect RAG solutions and AI agents on Azure AI Foundry and Power Platform, with grounded answers, tool use and human-in-the-loop steps. Set up evaluation frameworks with golden datasets, and use them to gate model and prompt changes. Design security and governance: Entra ID/RBAC, document-level access, Purview, audit logging, monitoring and cost controls, plus isolated deployments where required. Act as the client’s trusted technical advisor: run workshops and present trade-offs, cost and risk clearly. Support new client initiatives with estimates, RFP responses, demos, proposals and proofs of concept Mentor data and AI engineers through design reviews, code reviews and prototyping, and help grow the client’s in-house capability. What you need for this Tech stack: Microsoft Fabric / OneLake, Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure AI Document Intelligence, Power Platform and Dataverse, Microsoft Purview, Entra ID, Key Vault, Azure Monitor; integrations with SharePoint (Graph API), Salesforce and Procore. Requirements 8+ years in data engineering or data platforms, including 3+ years as a solution or data architect leading end-to-end designs for enterprise clients. 3+ years of Azure experience (Microsoft Fabric / OneLake, Azure AI Foundry and Azure OpenAI, Azure AI Search etc.) including security and governance (Entra ID, RBAC, Key Vault, private networking). Proven delivery of production data platforms (medallion patterns), experience in data modelling with lineage and provenance, data quality, entity resolution and master data. Delivered at least one generative AI solution (RAG, document AI or agents) beyond the prototype stage. Strong hands-on Python and SQL; working knowledge of PySpark. Ability to explain architecture and AI limits to non-technical business stakeholders, and to defend decisions with trade-offs, cost and risk. Comfortable working across several initiatives at once and switching between strategy and hands-on detail. English at B2+ (Upper-Intermediate) or higher for daily work with US stakeholders. Nice to have Experience in the AEC (architecture, engineering, construction) or life sciences domain: pre-construction, estimating, proposals, commissioning and qualification. Exposure to Procore, Salesforce, Cosential or similar construction and professional-services systems. Power BI / Microsoft Purview / Power Platform / Copilot Studio. Agentic frameworks (Semantic Kernel, LangGraph, Azure AI Foundry Agent Service), Model Context Protocol and LLM evaluation tooling. Infrastructure as code (Bicep or Terraform) and CI/CD for Fabric and Azure (Azure DevOps or GitHub Actions). Azure / Fabric certifications. What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

— Делать фичи поверх ядра: аналитические джобы, автоматизация, governance-сценарии.

🕹 Список обязанностей: — Развивать HR-платформу для всего OMNI и другие внутренние сервисы.

About the Role This is a hands-on engineering position combining software development, solution design, prototyping, and direct customer interaction. You will work closely with enterprise customers to understand their operational processes, existing systems, technical constraints, and business pain points — and then help translate those needs into practical technical solutions. The role is ideal for someone who is technically strong but also comfortable working directly with business and technical stakeholders. What You Will Do — Work directly with enterprise customers to understand workflows, systems, and pain points — Gather and clarify requirements with business and technical stakeholders — Translate ambiguous business needs into technical solutions — Build prototypes, integrations, and production-ready functionality — Design and implement APIs and system integrations — Work with databases and data integration flows — Support cloud-based solution design and implementation — Explain technical decisions, trade-offs, and architecture clearly to customers — Collaborate with internal engineering and delivery teams — Take ownership of solutions from initial discovery through implementation — Participate in customer meetings and occasional business trips to Paris MUST HAVE — Strong software engineering / programming background — Hands-on experience building production systems — Experience with APIs and system integrations — Strong understanding of databases and data integration — Experience with cloud platforms and services — Ability to prototype and implement solutions quickly — Strong problem-solving skills and ability to work with incomplete or evolving requirements — Excellent customer-facing communication skills — Confidence working with both technical and non-technical stakeholders — Ability to ask the right questions and explain technical concepts clearly — Availability for occasional business travel to Paris Nice to Have — AI / Machine Learning experience — LLM-based applications — AI Agents / Agentic workflows — RAG or GenAI integrations — Experience in consulting, solutions engineering, technical presales, or customer-facing engineering — Enterprise integration or digital transformation projects What We’re Looking For We are looking for someone who is not only a strong engineer, but also comfortable being close to the customer. You should enjoy understanding how a business actually works, identifying what needs to be improved, and turning that understanding into a practical technical solution. Conditions — Location: EU — Business travel: occasional trips to Paris, fully covered by the company What We Offer — Small-company feel within a fast-growing international environment — Friendly, collaborative, and mission-driven team — Fully paid business trips to Paris — 25 calendar days of vacation + 5 additional paid sick days — Medical insurance — Corporate English courses — Corporate events and team-building activities — Support with professional certifications — Reimbursement for professional courses and training — Long-term international projects — Opportunities for professional and technical growth

Location: Intellias Meet your recruiter Viktoriia Tochylova viktoriia.tochylova@intellias.com https://www.linkedin.com... Vacancy details AI/ML Engineering Machine Learning Engineer Senior Bulgaria, Croatia, Poland, Portugal, Spain Remote APPLY NOW REFER A FRIEND Over 20 years of market experience, Intellias brings together technologists, creators and innovators in Europe, North and Latin America, and the Middle East. Join our international team and take the mission to solve the advanced tech challenges of tomorrow! What project we have for you Join a transformative data and AI platform initiative aimed at modernizing enterprise-scale capabilities and enabling real-time decision-making. This project delivers a comprehensive roadmap covering AI, MLOps, data governance, and platform scalability, supporting a shift towards data-first operations and intelligent automation. What you will do Lead the ideation, design, and execution of AI proofs-of-concept and end-to-end machine learning systems, from research and experimentation through to scalable production deployment. Develop and implement machine learning and AI systems across domains including Computer Vision, NLP, and Generative AI, solving complex business and product challenges. Collaborate cross-functionally with engineering, product management, and business stakeholders to translate business objectives into scalable AI-driven capabilities and production-ready solutions. Architect and maintain production-grade AI systems across the full lifecycle, including data ingestion, feature engineering, model training, retrieval pipelines, orchestration layers, deployment, evaluation, and monitoring. Design and implement Retrieval-Augmented Generation (RAG) pipelines, semantic search systems, and multi-agent workflows, selecting appropriate architectures based on performance, latency, cost, and compliance constraints. Design evaluation frameworks for machine learning and generative AI systems, including offline evaluation, human-in-the-loop validation, automated scoring, and production monitoring. Optimize AI systems for scalability, latency, and cost efficiency, including batching, caching, quantization, and model selection strategies. Design and apply advanced mathematical models and statistical methods to analyze large, multi-modal datasets, including time series, classification, regression, and representation learning tasks. Collaborate on AI architecture decisions and contribute to technical roadmap planning, identifying high-impact opportunities for AI adoption across the organization. Mentor and provide technical guidance to junior ML engineers and data scientists, promoting engineering best practices and a culture of continuous learning. Stay current with emerging research, tools, and architectures in AI and machine learning, proactively evaluating and introducing relevant advancements. What you need for this Required skills: 5+ years of hands-on experience in machine learning, AI research, or data science, with a proven track record of delivering production-grade ML solutions. Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related quantitative field. Strong expertise across core ML disciplines including supervised and unsupervised learning, time series modeling, classification, regression, and model evaluation. Deep hands-on experience developing production systems in Computer Vision, NLP, and Generative AI. Strong experience building LLM-based applications, including RAG pipelines, agentic workflows, semantic search, and tool-using AI systems. Experience designing evaluation frameworks for generative AI systems, including prompt evaluation, hallucination detection, and performance monitoring. Proficiency in Python and modern ML ecosystem including PyTorch, TensorFlow, Hugging Face, NumPy, Pandas, and scikit-learn. Experience with modern LLM orchestration and serving frameworks such as LangChain, LlamaIndex, DSPy, Haystack, or equivalent. Experience deploying and scaling AI systems on cloud platforms (AWS, Azure, or GCP), including managed ML services and containerized deployments. Experience building and maintaining end-to-end ML pipelines, including data processing, model training, deployment, and monitoring. Understanding of responsible AI principles, including bias mitigation, safety guardrails, and governance considerations. Proficiency with version control (Git) and software engineering best practices for collaborative ML development. Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders. Will be a plus: Experience with MLOps tooling and workflows, including MLflow, Apache Airflow, Terraform, and CI/CD pipelines for model deployment. Experience with model serving and inference optimization tools such as vLLM. Experience working with feature stores, real-time data pipelines, or streaming AI systems. Experience with distributed training or large-scale model training frameworks. Experience with multimodal AI systems (vision-language models, audio-text, document AI). Experience with fine-tuning large language models or parameter-efficient training techniques (LoRA, PEFT, etc.). Experience contributing to open-source ML projects or publications in peer-reviewed venues. An Associate, Professional or Specialty-level certification from one of our strategic technology partners — AWS, Microsoft, Google, Databricks or Anthropic — aligned with your tech stack. What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Location: Intellias Meet your recruiter Khrystyna Derpak khrystyna.derpak@intellias.com https://www.linkedin.com... Vacancy details AI/ML Engineering Machine Learning Engineer Senior Bulgaria, Croatia, Poland Remote APPLY NOW REFER A FRIEND We are seeking a AI Engineer to transition our AI capabilities from “prototype” to “production.” In this role, you will not just experiment with models; you will architect robust Agentic Systems that can plan, reason, and execute complex workflows autonomously for a wide variety of business user needs while experimenting with cutting-edge models and tools to push the boundaries of what’s possible. The ideal candidate is a forward-thinking engineer who thrives on hands-on experimentation with AI models and frameworks, learns quickly, and is naturally curious. You will collaborate closely with product managers, business partners, and platform teams to deliver high-value AI agents that solve real-world problems across the enterprise. What project we have for you Client is a leading multi-brand technology solutions provider to business, government, education and healthcare customers in the United States, the United Kingdom and Canada. A Fortune 500 company and member of the S&P 500 Index, Client was founded in 1984 and employs approximately 10,000 coworkers. For the trailing twelve months ended September 30, 2020, the company generated Net sales over $18 billion. Broad array of offerings range from hardware and software to integrated IT solutions such as security, cloud, data center and networking What you will do Agentic Engineering & Orchestration Workflow Design: Architect complex, multi-agent workflows using Microsoft AI tech stack. Design, Develop and Deploy agents to handle loops, interruptions, and human-in-the-loop interventions. Tool Use & Function Calling: Build reliable “tool layers” that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms (e.g., SalesForce, Workday, ServiceNow etc.) State Management: Design persistence layers to manage agent memory, conversational history, and context windows efficiently. Advanced Data & RAG Strategy Retrieval Pipelines: Build production-grade data retrieval and integration systems. Optimize vector indexing, document chunking, and re-ranking algorithms to ensure high-precision context retrieval. Data Quality: Collaborate with Data Engineers to curate “Golden Datasets” for agent consumption LLMOps, Evaluation & Quality Automated Evaluation: Build CI/CD pipelines for AI that include “LLM-as-a-Judge” testing. Leverage frameworks to score agent outputs for accuracy, hallucination, and safety before deployment. Observability: Instrument applications with tracing tools to visualize agent reasoning chains, monitor latency, and debug failures in production. Cost Optimization: Monitor token usage and latency, optimizing prompt density and caching strategies to maintain high performance at sustainable costs. Innovation & Collaboration Prototyping to Production: Rapidly validate new ideas using state-of-the-art models, then refactor successful prototypes into maintainable, tested production code. Standards Adoption: Stay ahead of the curve by evaluating emerging technologies to standardize agent connectivity. What you need for this Core Engineering Bachelor’s degree and 5 years of software engineering experience, with exposure to AI/ML applications OR 9 years of software engineering experience, with exposure to AI/ML applications. Programming: Strong hands-on experience with Python for AI/LLM application development. Agentic AI: Hands-on experience designing and building AI agents / Agentic AI solutions. LLM APIs: Experience integrating and working with LLM model APIs (e.g., OpenAI, Azure OpenAI, Anthropic, Gemini). AI Specialization 2+ years specifically building with LLMs, with deep familiarity in: Orchestration: LangChain, LangGraph, or similar state-based frameworks. Vector DBs: Pinecone, Weaviate, or pgvector. Prompt Engineering: Advanced techniques (Chain-of-Thought, ReAct, Few-Shot). Production Mindset: Experience not just building demos, but operating them. You know how to handle rate limits, context window overflows, and non-deterministic errors. Soft Skills: Ability to explain “probabilistic software” to non-technical stakeholders—managing expectations that agents are never 100% accurate, but can be 100% useful. Communication: Excellent communication skills, with experience in documenting technical designs, sharing insights, and enabling team knowledge transfer. Nice to Have Microsoft Foundry What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

Location: Athens, Greece 🔎 Have you already built AI systems that other people use, and do you want to own them end to end, from the first measurement to production? Do you want to apply your machine learning experience to security-relevant AI? 💡 CENSUS Junior AI Engineer is your opportunity to join our AI team and own features in our production AI systems and products. You will build, evaluate, and ship LLM-powered features on security data, and help guide the interns on the team as you grow. Why Join? 🔹 Real Ownership: Own features in production AI systems end to end, from data and a first measurement through evaluation, deployment and iteration. 🔹 Elite Team: Work alongside world-class AI and cybersecurity experts. 🔹 Hands-on AI: High-impact work across LLM features, RAG and agent systems, evaluations, datasets, and fine-tuning . 🔹 Mentorship: A named AI-team mentor for your first months, and code review on every pull request. 🔹 Remote Work Model: Fully remote in Europe . 🔹 Growth: A clear path to AI Engineer as you take on larger, less defined work. What You’ll Do 🔹 Design, build and ship LLM-based features , together with the evaluations that prove they work. 🔹 Own work on retrieval (RAG), agent and tool-routing systems that answer questions over real security data. 🔹 Build datasets and fine-tune open models on security data, and measure the result against a baseline. 🔹 Scope your own tickets with your mentor and back every change with before-and-after results. 🔹 Review your teammates’ pull requests and help guide our interns. Who We’re Looking For 🚀 Required Skills: 🔹 At least 1 year of hands-on experience in AI, Machine Learning or Data Science engineering. Internships count, and so does industry or research-lab work. 🔹 A track record of shipping : at least one ML or LLM feature that went into production or into a codebase other people used. 🔹 Strong Python , with readable, tested code that others can review. 🔹 Experience in a team codebase: Git, pull requests , and both giving and receiving code review. 🔹 Practical experience building LLM applications (prompting, RAG or agents) and evaluating them, not only calling an API. 🔹 A solid grasp of machine learning fundamentals , and the ability to design an experiment and read its results honestly. 🔹 Working knowledge of SQL and Docker . 🔹 The ability to work independently on a well-scoped ticket and to say early when you are stuck. 🔹 Good written English (non-English resumes will be rejected) , and the habit of backing claims with evidence. ✨ Bonus Skills (Make Your Application Stand Out!): 🔥 Fine-tuning LLMs ( LoRA, SFT, DPO ) or building evaluation sets. 🔥 GPU training or model-serving experience (e.g. vLLM). 🔥 Vector databases , cloud or Kubernetes experience. 🔥 Published papers or research experience. 🔥 Interest in security or cryptography (we will teach you the domain). The Details 📌 Who Can Apply? 🔹 Open to graduates (Bachelor’s, Master’s or PhD) in Computer Science, AI, Machine Learning, Data Science or related fields, or candidates with equivalent practical experience, with at least 1 year of hands-on experience, internships included . 🔹 Europe-based candidates are prioritised. 📍 Location: Remote in Europe. 📄 Contract: Full-tiume 🎯 Career Path: Grow into an AI Engineer role as you take ownership of larger parts of our AI systems. About CENSUS CENSUS is a globally recognized cybersecurity firm delivering top-tier IT and OT security solutions. We work with Fortune 500 companies, financial institutions, critical infrastructure, and secure communications providers worldwide. Our expertise spans cutting-edge research, scientific analysis, and deep engineering experience, ensuring unmatched security for products, infrastructure, and organizations. 🔗 Learn more about us at CENSUS | Cybersecurity Engineering

Location: Москва Наша команда занимается разработкой умных решений для внутренних потребностей команд. В своих решениях используем как классические модели, так и ИИ-агентов на LLM GigaChat. Для выполнения задач мы собираем данные, готовим прототипы и валидируем разработанный инструмент. Наша цель – предоставить нашим пользователям качественные инструменты для решения текстовой аналитики, обработки и анализа данных и т.д. с применением передовых технологий в области ИИ, в том числе Generative AI. Обязанности * разработка бэкенда на Python * сборка и деплой приложений * проверка гипотез * подготовка датасета * разработка ИИ-агентов на LangGraph + GigaChat, промпт-инжиниринг, fine-tuning LLM * разработка RAG-пайплайнов. Требования * уверенное программирование на Python * опыт взаимодействия с LLM * использование и разработка REST API (aihttp, flask) * работа с БД (PostgreSQL) с использованием ORM * работа с асинхронным взаимодействием (kafka). **Будет плюсом:** * опыт участия в хакатонах * профиль на github * опыт решения алгоритмических задач (Leetcode, Codewars, Hackerrank и тд) * наличие законченных пет-проектов * навыки работы с RAG-пайплайнами * опыт в создании AI-агентов * работа с GigaChat * опыт работы с СУБД GreenPlum. Условия * офис: г Москва, проспект Кутузовский, 32 * 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 * 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 Ищем Системного аналитика в команду разработки AI Бизнес-помощника внутри интернет-банка для предпринимателей (СберБизнес). Цель нашего Помощника- взять на себя рутинные банковские операции, помочь подобрать сервисы, подходящие конкретному пользователю, подсказать возможные векторы развития его бизнеса на основе предыдущих результатов работы. Взаимодействие пользователя с Помощником происходит через чат, под капотом которого - LLM, RAG, MCP и, конечно, классические прикладные сервисы. Система семантически различает сотни смежных бизнес-сценариев, извлекает критическую информацию и подключает профильные GenAI-инструменты для выполнения операций. Над чем предстоит работать в ближайшее время: * научить систему лучше запоминать и использовать факты о пользователе, чтобы подстраиваться под его особенности и контекст * развивать платформу для агентов, которые действуют на опережение — сами инициируют полезные операции или предупреждают о важных событиях без явного запроса * усиливать безопасность и отказоустойчивость системы * совершенствовать архитектуру, чтобы компоненты и процессы координировались быстрее и точнее. Обязанности * выявление требований, разработка и согласование требований к программному обеспечению * управление требованиями на всём жизненном цикле разработки ПО * подготовка спецификаций и написание технической документации * предложения по улучшению существующих процессов или созданию новых * коммуникация между командами: координация взаимодействия внутри своей команды и со смежными подразделениями для эффективного выполнения поставленных задач * проработка архитектурных решений  * декомпозиция и постановка задач разработчикам * помощь в подготовке тест-кейсов * активное участие в планировании спринтов и продуктовых бэклогов (Agile/Scrum, Kanban). Требования * опыт описания и моделирования бизнес-процессов с использованием нотаций UML (sequence diagram) или BPMN, формулировки системных и функциональных требований * опыт написания технической и пользовательской документации * опыт проектирования реляционных баз данных или уверенное знание принципов проектирования * опыт проектирования интеграционных взаимодействий с нуля (REST, Kafka), базовые знания SQL и наличие опыта работы с базами данных * опыт работы с инструментами тестирования: Insomnia, Postman * уверенное владение инструментами для анализа и документирования: системы управления требованиями (Jira, Confluence, YouTrack), инструменты для построения диаграмм (draw.io, Miro, Lucidchart, PlantUML) * понимание полного жизненного цикла разработки ПО (SDLC) и практический опыт работы в Agile-командах. Будет преимуществом: * опыт работы в банковской сфере или участие в fintech-проектах * навык написания скриптов на Python для автоматизации рутинных задач (парсинг логов, сравнение данных, предварительная обработка) * теоретическое или практическое представление о векторном представлении данных (эмбеддингах) и методах кластеризации, понимание областей их применения (поиск похожих объектов, сегментация, классификация) * опыт проектирования систем с окрестратором или мультиагентных систем * формализация, постановка на мониторинг и отслеживание метрики качества ответов агентов * опыт применения современных LLM-инструментов. Условия * комфортный современный офис в Москве рядом с м. Кутузовский * 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.