Вакансии

Задачи: — развёртывать, настраивать и сопровождать Langfuse в защищённых контурах Банка, включая настройку зависимых компонентов: PostgreSQL, ClickHouse, Redis и объектного хранилища; — организовывать интеграцию с Apache Kafka: создавать и сопровождать топики, настраивать ACL, consumer groups, retention, репликацию, DLQ/retry-механизмы, мониторить Kafka lag; — поддерживать потоки телеметрии моделей: результаты работы классических ML-моделей, LLM traces, события RAG-пайплайнов, метрики качества, технические логи и алерты; — развёртывать и эксплуатировать сервисы в Kubernetes/OpenShift: namespaces, RBAC, Secrets, ConfigMaps, Ingress, resource limits/requests, autoscaling и сетевые политики; — разрабатывать и сопровождать Helm-чарты, Kubernetes-манифесты и шаблоны конфигурации для dev/test/preprod/prod-контуров; — строить и поддерживать CI/CD- и GitOps-конвейеры: сборка, тестирование, сканирование, публикация артефактов, деплой и rollback; — автоматизировать инфраструктуру и конфигурации с использованием Ansible, Terraform или аналогичных средств; — настраивать мониторинг, централизованное логирование и алертинг: Prometheus, Grafana, Alertmanager, ELK/Opensearch или аналоги; — сопровождать и проводить первичную оптимизацию PostgreSQL, ClickHouse, Redis, Kafka, Nginx/Ingress и смежных инфраструктурных компонентов; — обеспечивать отказоустойчивость, резервное копирование, восстановление и планирование ресурсов платформы; — участвовать в разборе инцидентов, готовить RCA, runbook’и и мероприятия по предотвращению повторных сбоев; — обеспечивать требования ИБ: управление доступами, секретами, сертификатами, сетевой сегментацией, аудитом и устранением уязвимостей; — вести эксплуатационную и техническую документацию по сервисам, интеграциям и процедурам сопровождения. Требования: — опыт работы DevOps/SRE-инженером от 5-ти лет; — практический опыт эксплуатации Kubernetes или OpenShift в production-среде: Helm, RBAC, networking, autoscaling, диагностика и настройка ресурсов; — уверенное знание Linux, Docker, Git, Bash и основ сетевого взаимодействия: TCP/IP, DNS, HTTP(S), TLS, reverse proxy; — практический опыт работы с Apache Kafka: топики, партиции, репликация, consumer groups, ACL, retention, мониторинг и устранение инцидентов; — опыт построения и сопровождения CI/CD-пайплайнов на Jenkins, GitLab CI, TeamCity, Argo CD или аналогичных инструментах; — знание и практическое применение Ansible, Terraform либо других IaC-инструментов; — опыт настройки мониторинга и observability: Prometheus, Grafana, Alertmanager, централизованное логирование, трассировка; — опыт эксплуатации PostgreSQL; понимание принципов работы ClickHouse и Redis; — умение автоматизировать рутинные операции с помощью Python и Bash; — понимание принципов высоконагруженных и распределенных систем; — знание подходов к безопасной эксплуатации: управление секретами, сертификатами, доступами, уязвимостями и журналированием действий; — навыки технической коммуникации: взаимодействие с командами разработки, архитекторами, ИБ, администраторами платформы и владельцами интеграций. Будет плюсом: — опыт работы в крупных FinTech-компаниях; — знание Groovy. Условия: — уровень зарплаты обсуждается индивидуально, есть доплаты за менторство и профактивности; — долгосрочные проекты от российских заказчиков, возможность менять направления, прозрачная система Performance Review; — релокейт между офисами, выбор формата работы (удалённо, офис, гибрид), адаптация для новых сотрудников; — доступ к корпоративному порталу, митапам, конференциям (и как гость, и как спикер); — ДМС со стоматологией, частичная компенсация спорта, бесплатный английский, оплачиваемый отпуск и больничные; — тимбилдинги, детские праздники, внутренние мероприятия; — офисы в Москве, Санкт-Петербурге, Казани, Нижнем Новгороде, Самаре.

Location: Lithuania OUR HIRING PROCESS: We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment. We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team. At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team. From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. INFUSE is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy We're looking for Senior AI Engineers to design, build and run LLM-powered agents and AI features in production. You'll work across the full lifecycle: from scoping a use case with stakeholders, through architecture and development, to deployment, evaluation and ongoing improvement. This is a hands-on role for engineers who think like product owners: you understand the business problem, define what success looks like in numbers, ship to real users, and own the result after launch. Our stack: Python and TypeScript/Node.js, PostgreSQL, Kafka, Docker, GitHub Actions, AWS. We run multiple LLM providers (Anthropic, OpenAI, Google, AWS Bedrock and others) behind a shared LLM gateway, with MCP for tool access. We work AI-native: agentic coding tools are part of how engineering gets done here, and we provide official Anthropic Claude certification for our engineers. As part of our team, your responsibilities in this role will include: Design and build LLM agents and agentic workflows: tool use via MCP, structured outputs, retrieval (RAG), multi-step orchestration, human review steps where needed. Build and maintain MCP servers and tool layers that give agents governed access to company data and systems. Own quality and cost: evals and golden sets, per-agent telemetry (tokens, cost, latency, accuracy), prompt and model versioning, provider failover and model routing. Ship through CI/CD like any other production service: tests and evals in the pipeline, containerized deploys, monitoring, rollbacks. Contribute to shared platform pieces: the agent template, the LLM gateway, observability. Work directly with business stakeholders to scope, prioritize and validate use cases. Push back when something doesn't need an agent. Use AI coding tools daily. Claude Code is our default; deep experience with a comparable agentic tool also works. You drive the tool, verify its output and catch its mistakes. Our ideal candidate possesses the following skills: 5+ years of software engineering experience, with production systems you've designed, shipped and supported. Proven experience building LLM-based agents or agentic workflows that reached production with real users. Hands-on experience with agent frameworks and SDKs: Claude Agent SDK, Claude Managed Agents, Pydantic AI, LangGraph or similar. You can also explain what you'd build without one. Hands-on experience with MCP and RAG: you've built MCP servers or equivalent tool layers, and retrieval pipelines over real, messy data. Experience with multiple LLM providers and with AWS Bedrock: model selection, routing, fallbacks, cost and rate-limit management. Strong Python and SQL. Comfortable in TypeScript/Node.js. Solid production engineering on AWS: Docker, CI/CD, testing, observability. Product thinking and business focus: you start from the problem and its cost, define success in measurable terms, and own outcomes. Daily use of agentic coding tools (Claude Code, Codex, Cursor or similar), with the judgment to verify what they produce. Clear communication with technical and non-technical stakeholders, in English. Nice to have: LLM observability or gateway tooling (Langfuse, LiteLLM, OpenTelemetry for LLM traces or similar). B2B contact and company data: enrichment, deduplication, matching. Cloud operations or FinOps: monitoring, incident handling, cost management. Event-driven or workflow-engine experience (Kafka, Temporal or similar). Experience replacing no-code or workflow-tool automations with production code. What we offer: Fully remote work format; A collaborative and creative environment that values initiative and experimentation; Paid time off and sick leave; Reduced working hours on Fridays during the summer; Days-off on US national holidays + your birthday; Schedule: 14 PM - 23 PM EEST (Kyiv time).

Location: Romania OUR HIRING PROCESS: We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment. We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team. At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team. From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. INFUSE is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy We're looking for Senior AI Engineers to design, build and run LLM-powered agents and AI features in production. You'll work across the full lifecycle: from scoping a use case with stakeholders, through architecture and development, to deployment, evaluation and ongoing improvement. This is a hands-on role for engineers who think like product owners: you understand the business problem, define what success looks like in numbers, ship to real users, and own the result after launch. Our stack: Python and TypeScript/Node.js, PostgreSQL, Kafka, Docker, GitHub Actions, AWS. We run multiple LLM providers (Anthropic, OpenAI, Google, AWS Bedrock and others) behind a shared LLM gateway, with MCP for tool access. We work AI-native: agentic coding tools are part of how engineering gets done here, and we provide official Anthropic Claude certification for our engineers. As part of our team, your responsibilities in this role will include: Design and build LLM agents and agentic workflows: tool use via MCP, structured outputs, retrieval (RAG), multi-step orchestration, human review steps where needed. Build and maintain MCP servers and tool layers that give agents governed access to company data and systems. Own quality and cost: evals and golden sets, per-agent telemetry (tokens, cost, latency, accuracy), prompt and model versioning, provider failover and model routing. Ship through CI/CD like any other production service: tests and evals in the pipeline, containerized deploys, monitoring, rollbacks. Contribute to shared platform pieces: the agent template, the LLM gateway, observability. Work directly with business stakeholders to scope, prioritize and validate use cases. Push back when something doesn't need an agent. Use AI coding tools daily. Claude Code is our default; deep experience with a comparable agentic tool also works. You drive the tool, verify its output and catch its mistakes. Our ideal candidate possesses the following skills: 5+ years of software engineering experience, with production systems you've designed, shipped and supported. Proven experience building LLM-based agents or agentic workflows that reached production with real users. Hands-on experience with agent frameworks and SDKs: Claude Agent SDK, Claude Managed Agents, Pydantic AI, LangGraph or similar. You can also explain what you'd build without one. Hands-on experience with MCP and RAG: you've built MCP servers or equivalent tool layers, and retrieval pipelines over real, messy data. Experience with multiple LLM providers and with AWS Bedrock: model selection, routing, fallbacks, cost and rate-limit management. Strong Python and SQL. Comfortable in TypeScript/Node.js. Solid production engineering on AWS: Docker, CI/CD, testing, observability. Product thinking and business focus: you start from the problem and its cost, define success in measurable terms, and own outcomes. Daily use of agentic coding tools (Claude Code, Codex, Cursor or similar), with the judgment to verify what they produce. Clear communication with technical and non-technical stakeholders, in English. Nice to have: LLM observability or gateway tooling (Langfuse, LiteLLM, OpenTelemetry for LLM traces or similar). B2B contact and company data: enrichment, deduplication, matching. Cloud operations or FinOps: monitoring, incident handling, cost management. Event-driven or workflow-engine experience (Kafka, Temporal or similar). Experience replacing no-code or workflow-tool automations with production code. What we offer: Fully remote work format; A collaborative and creative environment that values initiative and experimentation; Paid time off and sick leave; Reduced working hours on Fridays during the summer; Days-off on US national holidays + your birthday; Schedule: 14 PM - 23 PM EEST (Kyiv time).

Location: Ukraine OUR HIRING PROCESS: We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment. We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team. At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team. From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. INFUSE is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy We're looking for Senior AI Engineers to design, build and run LLM-powered agents and AI features in production. You'll work across the full lifecycle: from scoping a use case with stakeholders, through architecture and development, to deployment, evaluation and ongoing improvement. This is a hands-on role for engineers who think like product owners: you understand the business problem, define what success looks like in numbers, ship to real users, and own the result after launch. Our stack: Python and TypeScript/Node.js, PostgreSQL, Kafka, Docker, GitHub Actions, AWS. We run multiple LLM providers (Anthropic, OpenAI, Google, AWS Bedrock and others) behind a shared LLM gateway, with MCP for tool access. We work AI-native: agentic coding tools are part of how engineering gets done here, and we provide official Anthropic Claude certification for our engineers. As part of our team, your responsibilities in this role will include: Design and build LLM agents and agentic workflows: tool use via MCP, structured outputs, retrieval (RAG), multi-step orchestration, human review steps where needed. Build and maintain MCP servers and tool layers that give agents governed access to company data and systems. Own quality and cost: evals and golden sets, per-agent telemetry (tokens, cost, latency, accuracy), prompt and model versioning, provider failover and model routing. Ship through CI/CD like any other production service: tests and evals in the pipeline, containerized deploys, monitoring, rollbacks. Contribute to shared platform pieces: the agent template, the LLM gateway, observability. Work directly with business stakeholders to scope, prioritize and validate use cases. Push back when something doesn't need an agent. Use AI coding tools daily. Claude Code is our default; deep experience with a comparable agentic tool also works. You drive the tool, verify its output and catch its mistakes. Our ideal candidate possesses the following skills: 5+ years of software engineering experience, with production systems you've designed, shipped and supported. Proven experience building LLM-based agents or agentic workflows that reached production with real users. Hands-on experience with agent frameworks and SDKs: Claude Agent SDK, Claude Managed Agents, Pydantic AI, LangGraph or similar. You can also explain what you'd build without one. Hands-on experience with MCP and RAG: you've built MCP servers or equivalent tool layers, and retrieval pipelines over real, messy data. Experience with multiple LLM providers and with AWS Bedrock: model selection, routing, fallbacks, cost and rate-limit management. Strong Python and SQL. Comfortable in TypeScript/Node.js. Solid production engineering on AWS: Docker, CI/CD, testing, observability. Product thinking and business focus: you start from the problem and its cost, define success in measurable terms, and own outcomes. Daily use of agentic coding tools (Claude Code, Codex, Cursor or similar), with the judgment to verify what they produce. Clear communication with technical and non-technical stakeholders, in English. Nice to have: LLM observability or gateway tooling (Langfuse, LiteLLM, OpenTelemetry for LLM traces or similar). B2B contact and company data: enrichment, deduplication, matching. Cloud operations or FinOps: monitoring, incident handling, cost management. Event-driven or workflow-engine experience (Kafka, Temporal or similar). Experience replacing no-code or workflow-tool automations with production code. What we offer: Fully remote work format; A collaborative and creative environment that values initiative and experimentation; Paid time off and sick leave; Reduced working hours on Fridays during the summer; Days-off on US national holidays + your birthday; Schedule: 14 PM - 23 PM EEST (Kyiv time).

Требуется специалист на позицию Senior AI/ML Engineer. Требуемые навыки: — Python — OpenCV — Deep Learning — Компьютерное зрение — Agentic systems — FastAPI — Langgraph — DeepEval — Langfuse — vLLM Вакансия предполагает полный рабочий день с возможностью удалённой работы.

Location: Poland OUR HIRING PROCESS: We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment. We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team. At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team. From there on, it’s decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. INFUSE is committed to complying with applicable data privacy and security laws and regulations. For more information, please see our Privacy Policy We're looking for Senior AI Engineers to design, build and run LLM-powered agents and AI features in production. You'll work across the full lifecycle: from scoping a use case with stakeholders, through architecture and development, to deployment, evaluation and ongoing improvement. This is a hands-on role for engineers who think like product owners: you understand the business problem, define what success looks like in numbers, ship to real users, and own the result after launch. Our stack: Python and TypeScript/Node.js, PostgreSQL, Kafka, Docker, GitHub Actions, AWS. We run multiple LLM providers (Anthropic, OpenAI, Google, AWS Bedrock and others) behind a shared LLM gateway, with MCP for tool access. We work AI-native: agentic coding tools are part of how engineering gets done here, and we provide official Anthropic Claude certification for our engineers. As part of our team, your responsibilities in this role will include: Design and build LLM agents and agentic workflows: tool use via MCP, structured outputs, retrieval (RAG), multi-step orchestration, human review steps where needed. Build and maintain MCP servers and tool layers that give agents governed access to company data and systems. Own quality and cost: evals and golden sets, per-agent telemetry (tokens, cost, latency, accuracy), prompt and model versioning, provider failover and model routing. Ship through CI/CD like any other production service: tests and evals in the pipeline, containerized deploys, monitoring, rollbacks. Contribute to shared platform pieces: the agent template, the LLM gateway, observability. Work directly with business stakeholders to scope, prioritize and validate use cases. Push back when something doesn't need an agent. Use AI coding tools daily. Claude Code is our default; deep experience with a comparable agentic tool also works. You drive the tool, verify its output and catch its mistakes. Our ideal candidate possesses the following skills: 5+ years of software engineering experience, with production systems you've designed, shipped and supported. Proven experience building LLM-based agents or agentic workflows that reached production with real users. Hands-on experience with agent frameworks and SDKs: Claude Agent SDK, Claude Managed Agents, Pydantic AI, LangGraph or similar. You can also explain what you'd build without one. Hands-on experience with MCP and RAG: you've built MCP servers or equivalent tool layers, and retrieval pipelines over real, messy data. Experience with multiple LLM providers and with AWS Bedrock: model selection, routing, fallbacks, cost and rate-limit management. Strong Python and SQL. Comfortable in TypeScript/Node.js. Solid production engineering on AWS: Docker, CI/CD, testing, observability. Product thinking and business focus: you start from the problem and its cost, define success in measurable terms, and own outcomes. Daily use of agentic coding tools (Claude Code, Codex, Cursor or similar), with the judgment to verify what they produce. Clear communication with technical and non-technical stakeholders, in English. Nice to have: LLM observability or gateway tooling (Langfuse, LiteLLM, OpenTelemetry for LLM traces or similar). B2B contact and company data: enrichment, deduplication, matching. Cloud operations or FinOps: monitoring, incident handling, cost management. Event-driven or workflow-engine experience (Kafka, Temporal or similar). Experience replacing no-code or workflow-tool automations with production code. What we offer: Fully remote work format; A collaborative and creative environment that values initiative and experimentation; Paid time off and sick leave; Reduced working hours on Fridays during the summer; Days-off on US national holidays + your birthday; Schedule: 14 PM - 23 PM EEST (Kyiv time).

Задачи: — Повышение качества RAG-агентов по трём направлениям: поиск по технической документации, нормативной базе, бухгалтерской документации; — Промпт-инжиниринг и контекст-инжиниринг для всех агентов команды; — Подготовка эталонных наборов оценки качества; — Реализация LLM-as-a-judge для автоматических регрессионных проверок; — Настройка защит для production-агентов: фильтры от инъекций в промпт, валидация структуры и контента выходов, защита от утечек персональных данных, anti-hallucination механизмы; — A/B-тестирование промптов и моделей для подбора лучших конфигураций; — Тюнинг качества по сигналам обратной связи пользователей и трассировкам из Langfuse. Требования: — Python от 2 лет коммерческой разработки; — Практический опыт RAG-систем в продакшене: эмбеддинги, векторные базы (Qdrant, FAISS или pgvector), переранжирование, чанкинг; — Опыт построения и поддержки как минимум одного RAG-решения от начала до конца, не прототип; — Практический опыт оценки качества LLM-систем: подготовка эталонных наборов, offline-метрики, LLM-as-judge, регрессионные проверки; — Опыт работы с фреймворками оценки (Ragas, DeepEval или аналоги); — Практический опыт настройки защит для LLM-приложений: защита от инъекций в промпт, валидация структуры и контента выходов, защита от утечек персональных данных; — Опыт промпт-инжиниринга и контекст-инжиниринга в реальных проектах: итеративная настройка промптов, structured output, function calling; — Понимание архитектуры RAG: стратегии нарезки документов, метаданные, выбор моделей эмбеддингов, переранжирование, точность ссылок на источник; — Практический опыт работы хотя бы с одним LLM-фреймворком: LangChain, LangGraph, PydanticAI, OpenAI API или аналоги; — Практический опыт A/B-тестирования промптов и моделей в продакшене; — Опыт работы с агентскими протоколами (MCP) или собственным tool-layer для агентов; — SQL и работа с реляционными базами на базовом уровне.

Location: Limassol; Nicosia; Athens; Thessaloniki; Fully Remote - in other eligible locations Workplace: hybrid The Role:  We're seeking a Senior AI QA Test Automation Engineer to take a leading technical role in AI quality and test automation. You will work primarily with our AWS Bedrock AgentCore-based CX Agent Suite, a multi-agent customer-support system covering intent routing, FAQs, deposit-status queries, and human handoff. You will design and build the evaluation and QA tooling required to validate AI agents reliably, while also contributing to technical standards and best practices across QA.  This is a Senior Individual Contributor (IC) role with a strong technical and strategic focus. You'll work closely with QA leadership, Data Science, and Engineering to architect scalable AI evaluation systems, establish technical standards, and drive the evolution of our AI evaluation framework. Our current evaluation harness is based on DeepEval and is evolving towards a broader agentic evaluation architecture. You will have the opportunity to evaluate and prototype emerging agent-orchestration approaches, including Strands Agents, and contribute to architectural decisions around their adoption. The main responsibilities of the position include: Act as the primary technical enabler for QA, building scalable AI/ML frameworks, libraries, and tooling for the CX Agent Suite and beyond Design and build AI evaluation pipelines that assess the CX Agent Suite's multi-agent responses (Intent Detector, FAQ Agent, Missing-Deposits Agent) for accuracy, relevance, tone, hallucination rate, safety/guardrail compliance, and task completion — extending or replacing the current DeepEval-based evaluation harness Evaluate and prototype Strands Agents (or comparable agent-orchestration frameworks) for building self-evolving, autonomous QA agents, and drive the go/no-go decision on adoption Collaborate with QA, Data Science, and Engineering to integrate AI-driven testing into the existing GitLab CI/CD pipeline (dev → test → staging → prod), alongside Terraform-provisioned, EKS/AgentCore-hosted services Build resilient and adaptive automation that can detect and respond to changes in agent behavior, Bedrock Guardrails configuration, and routing logic Develop data-driven quality analytics, including root-cause analysis, quality trends, and intelligent test prioritization, leveraging existing observability and evaluation data from OpenTelemetry, CloudWatch, X-Ray traces, and LangFuse offline evaluation runs Lead research into emerging AI testing methodologies and mentor engineers through code reviews, workshops, and architectural guidance Main requirements: BSc/MSc in Computer Science, AI, or a related discipline 6+ years of hands-on experience in QA/Test Automation, with strong experience designing and maintaining automation frameworks 1+ years of experience applying AI/ML in software testing or QA process improvement, with a proven track record of bringing AI/ML solutions into production workflows Strong Python skills, with experience working in Python/AWS-centric technology environments; Java and/or TypeScript is a plus Hands-on experience with LLM evaluation techniques, including LLM-as-a-judge, human-in-the-loop evaluation, RAG, and multi-agent orchestration patterns Practical experience with DeepEval or comparable evaluation frameworks, along with familiarity with agent-orchestration SDKs such as Strands Agents Knowledge of LLM tooling, vector databases, and MLOps pipelines Experience integrating AI tooling into enterprise CI/CD environments, particularly GitLab, and working with containerized cloud-native environments such as Docker and Kubernetes/EKS Strong communicator, able to influence technical decisions and drive engineering standards across teams The following will be considered an advantage: Experience with autonomous QA agents or agentic orchestration frameworks for self-evolving test suites Experience with LLM observability tools such as LangFuse, LangSmith, or Arize, particularly for measuring probabilistic and adversarial robustness Knowledge of AI ethics, fairness, and bias detection for guardrail and model validation Experience with gRPC, WebSockets, and/or HTTP/2 Experience with AWS Bedrock, plus familiarity with GCP Vertex AI and/or Azure AI Benefit from: Attractive remuneration package Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy and Data Protection Notice and the Confidentiality Undertaking.

Location: Americas; EMEA Workplace: Remote RevenueCat gives app businesses the infrastructure and tools to build, run, and improve their monetization. Since graduating from YC's S18 batch, we've grown into the default monetization platform for mobile. We're in 50%+ of newly shipped subscription apps, we process $16B+ in annual purchase volume, and we help everyone from a solo developer to the OpenAI mobile team understand and grow their revenue. We're a remote-first team of 150+ people across 25+ countries, guided by values we actually practice: Customer Obsession, Always Be Shipping, Own It, and Balance. This isn't the right fit for everyone. The systems you’ll build here manage billions of dollars and touch hundreds of millions of end users, and we don’t take that lightly. You'll be expected to work hard and hold a high bar for what you ship, alongside some of the sharpest, most driven people you've worked with. If that sounds energizing rather than exhausting, keep reading. THE ROLE RevenueCat helps app developers manage subscriptions and make more money from them. Maybe you're an indie developer who doesn’t know what "conversion" even means, you just want your app to make money so you can keep building it. Or maybe you're a growth specialist at a company doing millions a month, and pricing experiments are already your whole job. Either way, you probably didn't get into this to spend your days staring at a dashboard. So we built two AI agents to do that work for you, or make you even sharper at it if you're already a pro. Enter Rico. Rico watches over your subscription business and can help you make sense of it, and act on what it finds. Say your revenue dropped last week. You don't need to go digging through charts to find out why. Just ask Rico. It pulls the data, explains what happened in plain language, and shows you how you compare to similar apps. Then it can fix things itself, like setting up a new plan or restoring a customer's access, with you approving the change before it goes live. Run a pricing experiment and Rico will read the results for you and tell you which version is winning. There are so many things we want Rico to be able to do! Then there's our AI Paywall Editor, a specialized agent to build and edit paywalls, the screen that asks someone to subscribe. Most developers are not designers, and a paywall that actually gets people to tap "subscribe" is harder to build than it looks, so the AI Paywall Editor helps you with that. Use it in our online editor, from the command line, or hook your own AI tools into it over MCP. You can see both agents in action in our most recent product launch recap https://www.revenuecat.com/blog/engineering/launch-party-june-26-the-new-features-you-should-know. This is a role for engineers who've built advanced agents that do actual work, not just a thin wrapper around an LLM. You'll ship agentic features to thousands of developers, and you’ll shape the foundation everything else runs on: orchestration, tool execution, context management, evaluation and human-in-the-loop controls. ABOUT YOU - You have 8+ years of experience shipping production systems. - You've built agents that other people actually use and the systems around them: tool use, structured output, context management, evaluation, orchestration. - You have strong backend fundamentals. You've built systems that need to be reliable, observable, and secure. - You hold a high bar for quality and instinctively know which details matter, but also are discerning enough to cut scope when needed while still delivering value. - You’re customer-obsessed: you imagine yourself as the developer, product manager, or marketer using the feature or experiencing the issue. - You’re a builder, not an implementer: scoping, prioritizing, and driving rollout in partnership with our Product team. This isn’t a role where you check off items from a detailed specification. - You can quickly familiarize yourself with code you didn't write yourself and improve it. - You’re scrappy and biased toward shipping. You’d rather ship a good version today and iterate than wait to ship a perfect one next quarter. - You communicate clearly, both in real-time and asynchronously. We’re a fully remote team, and how you write is as important as how you code. - You’ve shipped meaningful user-facing features to production, monitored their usage and adoption, and improved them over time. - You write high-quality, well-tested code, debug issues methodically, and help raise the engineering bar through thoughtful feedback and mentorship. - You act like an founder:: you notice problems, propose improvements, and follow through fast. - You're comfortable with ambiguity. This is an early stage product inside a growth stage company, and the architecture is still being figured out. - You're self directed. You figure out what needs to be built, build it, and ship it. - You are curious and stay current on AI tools and practices. - You have a habit of sharing what you learn with others. IDEALLY, YOU HAVE: - Familiarity with subscription business models, app stores, or developer tools - Experience with OpenAI/Anthropic APIs specifically - Experience with observability frameworks (Langfuse, Langchain, etc.) - Built Slack integrations - Contributed to open source AI/ML tooling - Maintained systems at scale IN THE FIRST MONTH, YOU’LL: - Meet frequently with your team and manager to get up to speed - Learn our tools, processes, and the subscription business concepts that shape our product - Get familiar with our debugging tools, infrastructure, and overall architecture - Ship your first project: a small feature that gets you into the codebase and shows you how the agents talk to RevenueCat's APIs - Jump in early: fix bugs, ship small improvements, and weigh in on PRs WITHIN THE FIRST 3 MONTHS, YOU’LL: - Ship a medium sized project, ideally a new agentic feature that reaches tens of thousands of developers - Understand the main product areas your team owns and how they work end to end - Understand our current agent infrastructure, orchestration, the eval pipeline, the tool framework, and the trust and permissions layer, and have opinions on how to improve it - Scope and work on projects on your own, from idea to post launch improvements - Take part in code reviews, bug bashes, and technical discussions WITHIN THE FIRST 6 MONTHS, YOU’LL: - Become the go to engineer for certain focus areas - Have made the agents measurably more reliable, faster, and capable - Contribute to architecture discussions that affect more than just your team - Mentor other engineers joining the team - Talk directly with customers to understand how your work actually lands - Debug issues across frontend, backend, and data. Communicate clearly during incidents and drive fixes that prevent repeats - Learn the basics of incident response and join the on call rotation WITHIN THE FIRST 12 MONTHS, YOU’LL: - Have built infrastructure the rest of the team ships agents on top of - Know all the major components of our system and be able to debug complex issues - Write proposals and drive major architecture discussions - Spec and architect large projects, gather feedback, and drive design validation and rollout plans - Have made RevenueCat an AI native company in large part because you built the engine underneath it - Have your own initiatives for improving our product and systems, grounded in real customer priorities WHAT WE OFFER: - Competitive equity in a fast-growing, Series C startup backed by top-tier investors, including Y Combinator - 10-year window to exercise vested equity options - A fully remote environment designed around autonomy and flexibility - 4-5 weeks of flexible time off annually - Paid desk at a co-working space - Workspace budget and continuous learning stipend INTERVIEWING AT REVENUECAT Our interview process is rigorous on purpose. We want to be sure that everyone we bring on is genuinely excited about this work, aligned with how we operate, and ready to meet our high bar for performance. Curious what that actually looks like? Read more in our blog post on how we hire at RevenueCat https://www.revenuecat.com/blog/company/how-we-hire-at-revenuecat/, including tips to help you succeed. MORE ON HOW WE WORK: - How we think about hiring and team design: Building a Winning Team https://app.notion.com/p/Building-a-Winning-Team-69e431c4c3df4b13961523cd93c0e9bf?pvs=21 - The values that guide how we work: Our Values https://www.revenuecat.com/blog/values/ - How we work as a remote team: How We Work Remotely at RevenueCat https://www.revenuecat.com/blog/how-we-work-remotely-at-revenuecat/ - What to expect from our interview process: Interview Expectations https://app.notion.com/p/RevenueCat-Interview-Expectations-1bdcb1a108b0800c8941def0f076f9e2?pvs=21 - What to expect from comp and employment: Employment FAQ https://app.notion.com/p/RevenueCat-Compensation-Employment-Overview-184cb1a108b0801398d4e525928597ab?pvs=21

Location: EMEA Workplace: Remote ClickHouse welcomed Langfuse, the leading open-source platform for tracing, evaluation, and observability, to strengthen its position in the AI infrastructure and LLM observability ecosystem. With ClickHouse and Langufuse scaling to power enterprise AI workloads, we're growing our support team to provide a world-class experience for our expanding customer base, with unified expertise across database infrastructure and AI observability. We are seeking a Technical Customer Support Engineer to join our global Support Engineering team in a hybrid role spanning both ClickHouse and Langfuse ecosystems. In this position, you will support customers across EMEA and globally in the Americas and APJ when support is needed or coverage overlaps. You’ll be handling technical issues and solutions, ranging from core ClickHouse database requests to helping thousands of AI product teams succeed with tracing & evals in Langfuse. You will own a wide array of responsibilities, including front-line troubleshooting, community support, pre- and post-sales technical activities, and partnering on deeply technical POCs, and suggesting improvements to how we do things in Support at ClickHouse. What You'll Be Doing - Supporting and guiding our ClickHouse & Langfuse users, customers, and prospects via cases, chat, Slack, community, and phone calls, delivering high-touch guidance within required Service Level Agreements (SLAs) as part of a global on-call coverage rotation. - Develop solutions based on ClickHouse Cloud and ClickHouse open-source that can be shared with our users, community, and customers via documentation, knowledge base, blogs, meetups, webinars, and training. - Work closely with our global Support Services, Engineering, Go-to-Market, and Product Management teams to help define the functionality required by users and customers. - Assist with mentoring, training, and sharing your knowledge with colleagues, users, and customers. - Escalate bugs to product engineering and close the loop with the customer once shipped. - Go through deep technical integration/implementation questions together with our customers and directly within their codebases. - Build strong, trusted relationships with colleagues, customers, and partners across the developer ecosystem What You Bring For ClickHouse & Core Infrastructure: - Databases & OLAP Systems: Hands-on experience with databases, OLAP, or distributed systems (ClickHouse, SQL engines, or cloud-native SaaS infrastructure). - Systems Fundamentals: Strong software engineering fundamentals (CS, Data Science degree, or comparable hands-on project experience) with comfort reading code, working with data pipelines (e.g., Kafka, Spark), or debugging cloud infrastructure (AWS/GCP/Azure). - Distributed & Operational Rigor: Comfort working in a global, remote-first team aligned to SLA coverage, on-call needs, and async communication. For Langfuse & AI Observability: - LLM Tooling & Application Dev: Genuine curiosity for LLM application development with hands-on experience building, debugging, or evaluating LLM-powered products using frameworks like Python, TypeScript, LangChain, LlamaIndex, or Vercel AI SDK. - Tracing, Evals & Developer Tooling: Familiarity with LLM observability/eval tooling, prompt workflows, model provider APIs (OpenAI, Anthropic), or OpenTelemetry-style tracing to unblock AI-product teams. - Technical Support & Communication: Strong written communication across registers. You have concise Slack troubleshooting, clear long-form docs/guides, and a customer-first mindset to unblock engineers. Bonus Points - Experience with OSS and open-source technologies, as a user, community member, or contributor - Experience with ClickHouse - Experience with Langfuse specifically, or with LLM observability/eval tooling more broadly. - Experience with AWS, GCP, or Azure. - Experience with data pipeline technologies such as Kafka, Kinesis, Spark, or RabbitMQ. PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new team member who joins our company receives stock options. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Задачи: — Разрабатывать AI-ассистентов, чат-ботов и AI-агентов. — Создавать многоэтапные AI-workflow. — Интегрировать OpenAI, Anthropic и другие LLM API. — Работать с context, memory, state и tool calling. — Разрабатывать AI-решения для Telegram, WhatsApp и Web. — Автоматизировать бизнес-процессы с помощью AI. — Разрабатывать и улучшать RAG-системы. — Работать с embeddings, semantic search, hybrid search и reranking. — Настраивать обработку, chunking и индексацию документов. — Работать с векторными, SQL- и NoSQL-базами данных. — Реализовывать цитирование и проверку источников ответов. — Разрабатывать backend на Python / FastAPI и создавать REST API. — Работать с Pydantic и асинхронным Python. — Интегрировать внешние API и базы данных. — Реализовывать retry, timeout, error handling и logging. — Контейнеризировать сервисы через Docker. — Оптимизировать промпты и системные инструкции. — Создавать тестовые сценарии и evals. — Выявлять галлюцинации и проблемы с контекстом. — Проводить регрессионное тестирование AI-функциональности. — Сравнивать модели по качеству, скорости и стоимости. — Контролировать token usage, latency, ошибки и расходы на LLM. — Анализировать реальные диалоги пользователей и улучшать AI-системы. Требования: — От 2 лет коммерческого опыта в AI Engineering, Backend, ML Engineering или смежной технической роли. — Уверенный Python. — Практический опыт работы с FastAPI. — Опыт работы с REST API, Pydantic и асинхронным Python. — Опыт работы с Docker и Git. — Практический опыт интеграции OpenAI, Anthropic или других LLM API. — Опыт работы с LangChain, LangGraph или аналогичными инструментами. — Понимание RAG, embeddings и semantic search. — Опыт разработки AI-ассистентов, чат-ботов или AI-агентов. — Опыт работы с SQL и/или NoSQL. — Понимание ограничений LLM: галлюцинации, context window, latency и стоимость. — Умение писать структурированный и поддерживаемый код. — Способность самостоятельно разбираться в задачах и предлагать технические решения. — Английский язык. Будет большим плюсом: — Qdrant, Pinecone, pgvector, Weaviate, Milvus или другие vector DB. — Production-ready RAG. — AI Agents и tool calling. — Structured Outputs, JSON Schema и Function Calling. — LLM evaluation / automated evals. — LangSmith, Langfuse, Arize Phoenix или аналогичные инструменты. — PostgreSQL, Redis, Elasticsearch / OpenSearch. — CI/CD. — AWS, GCP, Azure. — OCR и обработка документов. — Опыт разработки multi-tenant SaaS. — Понимание информационной безопасности и защиты данных. AI-инструменты в разработке: — Claude Code — Cursor — GitHub Copilot — Codex и аналоги Ожидания на первые 3 месяца: — Разобраться в существующей AI- и backend-архитектуре. — Самостоятельно развивать LLM-функциональность. — Реализовать минимум одну значимую AI-функцию от идеи до production. — Улучшить качество, стабильность, скорость или стоимость существующих AI-сценариев. — Настроить базовое тестирование и оценку качества AI-ответов. — Улучшить логирование и мониторинг ключевых AI-сценариев. Условия: — Полная или частичная занятость. — Формат: удалённо / гибрид. — График: 5/2 и другие варианты. — Рабочий день 8 часов. — Оформление по трудовому договору. — Остальные условия обсуждаются индивидуально с финальным кандидатом. — Работа над production-ready AI-системами, а не только демонстрационными решениями.

Задачи: - Разрабатывать бэкенд LLM-агента для автоматизации поддержки - Учиться строить LLM-продукты и участвовать в формировании платформы для их развития - Следить за надежностью и качеством решений — агент будет работать под нагрузкой и решать проблемы миллионов пользователей - Решать задачи на стыке с Data Science, собирать легкие прототипы новых фич для проверки на пользователях - Следить за надежностью и качеством решений — внутренние инструменты работают с теми же гарантиями, что и внешние: мониторинг, алертинг, SLA Требования: - Опыт разработки на Python с использованием FastAPI, Pydantic, LangChain, LangGraph, LangFuse, punq - Опыт разработки распределенных систем: Kafka, PostgreSQL, Redis, Elastic - Опыт разработки сложных распределенных систем - Способность быстро выполнять задачи без критических ошибок - Приветствуется опыт работы с ML-продуктами и интерес к ML, NLP, LLM

MDiet — early-stage HealthTech стартап, разрабатывающий AI-powered платформу, которая помогает снижать количество повторных госпитализаций пациентов. Продукт ориентирован на рынок здравоохранения США. Возможна релокация в США при успешном росте компании, обсуждается индивидуально. Tech Stack: • LangGraph + LangChain / Agentic AI • vLLM + Hugging Face • PyTorch + PEFT / LoRA / QLoRA • Vector databases: pgvector, Qdrant, Weaviate, Chroma, Pinecone • Advanced RAG, Prompt Engineering, Embeddings • FastAPI + PostgreSQL • Docker + Kubernetes + async processing: Arq / Celery / Redis Streams • Apache Airflow для data pipelines • AWS Cloud: EC2, S3, SageMaker, Lambda, RDS, Bedrock • Structured output: Outlines / Guidance / Instructor • Evaluation & observability: Langfuse, RAGAS / DeepEval, Prometheus + Grafana • MLOps practices Задачи: • Проектирование и разработка multi-step LLM agents и pipelines для обработки медицинских данных • Разработка production RAG-систем с высоким уровнем grounding и качеством citation • Деплой и оптимизация self-hosted LLM inference через vLLM с фокусом на cost и throughput optimization • Fine-tuning open-source моделей на domain-specific medical data с измеримым улучшением результата • Построение data pipelines и versioning для документов, embeddings и training data • Создание evaluation frameworks, guardrails и систем контроля hallucinations • Реализация reliable structured output и data extraction • Тесное взаимодействие с physicians и Platform Architect Требования: • Сильный hands-on опыт с production LLM systems: RAG, agents, inference • Опыт fine-tuning open-source LLMs: LoRA / QLoRA / PEFT, с измеримыми результатами • Опыт построения data pipelines и versioning для LLM / RAG workloads • Сильные навыки в evaluation, hallucination reduction и structured output • Production Python + FastAPI + async processing • Опыт с self-hosted inference: vLLM или аналогичные решения • Опыт GPU optimization • Английский язык на уровне C1: strong written and spoken communication skills • Bachelor’s degree или выше в Computer Science, Artificial Intelligence, Machine Learning, Data Science или смежной технической области • 5+ лет опыта в ML/AI • 1.5–2+ года опыта в production LLM projects • Опыт с MLOps Будет плюсом: • Опыт в medical / clinical NLP или regulated domains • Cost modeling и optimization of LLM workloads • MLOps и production fine-tuning pipelines • Apache Airflow, Prometheus / Grafana или AI Solution Architecture • Publications in relevant fields Условия: • Работа в early-stage HealthTech startup с продуктом для US healthcare market • Возможность участвовать в создании AI-powered платформы с реальным влиянием на healthcare-процессы • Офисный формат в Астане первые 7–9 месяцев, далее возможен hybrid/remote • Зарплата $4 000 – $5 000 USD gross + equity • Возможность релокации в США при успешном росте компании, обсуждается индивидуально

Задачи: — Проектировать и владеть сквозными ML и дата-системами от сбора данных до продакшн-деплоя — Архитектурно создавать масштабируемые дата-пайплайны для RAG, эмбеддингов и обработки в реальном времени — Строить и эксплуатировать ML-сервисы, API и инфраструктуру с использованием IaC, контейнеризации и оркестрации — Интегрировать ML-системы с внешними API, инструментами и платформами для автоматизации — Устанавливать мониторинг, оценку и наблюдаемость данных, моделей и систем — Наставлять инженеров и задавать техническое направление команды Требования: — Опыт проектирования и масштабирования продакшн ML-систем и дата-платформ — Глубокие знания в области дата-инжиниринга, ML пайплайнов, RAG и эмбеддингов — Опыт создания надежной инфраструктуры данных с гарантией качества — Навыки Python и SQL, Docker, Kubernetes, облачные среды — Опыт работы с IaC (Terraform) и воспроизводимыми масштабируемыми системами — Практический опыт интеграции ML с API/сервисами и продакшн-операциями с мониторингом Желательно: — Опыт работы с LLM, агентными архитектурами (LangGraph, AutoGen, CrewAI) — Знакомство с синтетическими данными, системами оценки, AI feedback loops — Инструменты наблюдаемости Agent/ML (Langfuse, LangSmith) — Обслуживание моделей, маршрутизация, оптимизация инференса — Опыт с open-source моделями (LLaMA, Mistral) или кастомным инференсом — Паттерны надежности и безопасности систем AI Условия: — Гибридный формат работы: 2 дня в неделю на кампусе в Берлине — Поддержка релокации в Берлин — 27 дней отпуска + дополнительные дни после 2-3 лет — Бюджет на образование €1,000, языковые курсы, Udemy, поддержка родителей — Медосмотры, медитация, субсидии на спорт и велосипед — План акций для сотрудников, банк саббатикала, транспортные скидки, страховка, пенсия — Ваучеры на питание, корпоративные скидки

Задачи: — Проектировать и развивать внутреннее AI-ядро: агенты, skills, tool use, routing, orchestration. — Строить и улучшать RAG-сценарии: retrieval, chunking, embeddings, reranking, доступ к знаниям. — Делать reusable-компоненты и шаблоны для аналитиков: skills, коннекторы, пайплайны, reference flows. — Интегрировать AI-платформу с внутренними системами, базами данных, API и корпоративными источниками знаний. — Настраивать observability, tracing, evaluation и контроль качества AI-пайплайнов. — Оптимизировать качество, latency, стоимость и устойчивость пайплайнов. Требования: — Практический опыт построения production RAG / agent / workflow-систем. — Опыт создания внутренних инструментов или платформ, которыми реально пользовались аналитики или бизнес-команды. — Умение работать на стыке engineering и enablement: собрать решение и сделать его понятным для пользователей. — Опыт интеграции AI-пайплайнов с корпоративными источниками данных, API и внутренними системами. — Умение разбирать сложные пользовательские сценарии и упаковывать их в reusable-механики платформы. Технический стек: • Python, SQL, FastAPI, API integrations, async, service-oriented development. • LangGraph, LlamaIndex, Haystack, Semantic Kernel. • Qdrant, pgvector, Weaviate, Milvus, OpenSearch, Elasticsearch. • Embeddings, hybrid search, metadata filtering, reranking. • Airflow, Prefect, Dagster, Temporal. • Langfuse, Phoenix, OpenTelemetry (или собственные подходы к tracing / observability / eval). • Docker, Linux, Kubernetes, CI/CD. Условия: — Ставка: обсуждается отдельно. — Готовность к редким командировкам в Ереван. — Работа в топовом финтех-направлении над уникальным AI-продуктом.

Location: San Jose, California, United States Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. About the Opportunity We are seeking a Principal Engineer with a deep expertise in autonomous AI agent architecture and deployment, to spearhead the design, development, and optimization of intelligent agent systems on our global crypto exchange platform. This is a senior role requiring a hands-on AI science background combined with strategic oversight, technical direction, and cross-functional collaboration. You will work at the cutting edge of AI and crypto, designing AI agents that can autonomously reason, act, and evolve in complex, real-time environments such as trading, security, compliance, and customer interaction. What You’ll Be Doing Lead the design, development, and lifecycle management of autonomous AI agents tailored to crypto exchange operations (e.g., customer assistant, trading bots, risk detection agents, fraud prevention agents, etc.). Architect multi-agent systems (MAS) and frameworks to support goal-directed, reasoning, planning, and reinforcement-learning-based agents. Collaborate with product, engineering, business, and operation teams to define agent roles and measurable KPIs. Drive experimentation, simulations, and continuous learning pipelines for agent performance optimization. Stay ahead of the curve on developments in AGI, LLM-integrated agents, and cognitive architectures; apply relevant innovations to production systems. Mentor and lead a team of AI scientists and engineers; help define the AI roadmap and research strategy. Ensure system scalability, security, and regulatory compliance of AI-powered solutions in the crypto exchange domain. What We Look For In You 10+ years of industry experience in a relevant field, or an equivalent combination of advanced degree (PhD/Master's in Computer Science, AI, Machine Learning, Robotics, or related field) and industry experience. Proven experience building and deploying autonomous AI agents in live environments. Ideal candidates will have: Designed multi-agent frameworks or agent ecosystems (e.g., AutoGPT, OpenAgents, LangGraph, LangFuse, Dify, Coze, etc.). Experience with goal-oriented agents, context engineering, memory management, tool use, and RL-based decision making. Developed agents for real-time decision-making, such as trading, gaming, customer support, or operations. Track record of delivering end-to-end AI systems in fast-paced, high-scale environments (preferably finance, crypto, or high-frequency domains). Nice to Haves Experience in top-tier AI labs or Tech industry leaders Experience in high-frequency trading, fintech, Web3, or crypto platforms is a strong advantage Startups or research-intensive environments preferred; must be comfortable operating with ambiguity and speed OKX Statement The salary range for this position is $250,444.00 to $375,666.00 . The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via Okcoin and OKX internal or external careers site. OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. #LI-HYBRID#LI-HYBRID #LI-ED1 Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Задачи: — Лидирование команды разработчиков и QA (10+ человек). — Разработка архитектуры высоконагруженных систем. — Внедрение и использование AI-инноваций (мониторинг новых технологий, их интеграция). — Проведение code review, установление стандартов разработки. — Hands-on участие в разработке, устранение блокеров. — Найм, онбординг и удержание талантов. — Системное ведение проектной документации (Jira, канбан). — Взаимодействие с PM, DevOps и ML-командами. — Фокусировка на product-thinking, метриках, пользовательском опыте и качестве. Требования: — Опыт работы в разработке от 8 лет (fullstack JS, backend/frontend/DevOps). — Опыт лидирования команд от 5 лет (10+ человек) без микроменеджмента. — Экспертиза в разработке высоконагруженных архитектур. — Глубокое понимание и практический опыт работы с AI (LLM infra, fine-tuning, RAG, embeddings, vector DBs, Voice AI, CV, multimodal). — Хард скиллы: Node.js, React, Next.js, React Native/Expo, GraphQL/REST/WebSockets/OAuth2/JWT, Python/FastAPI. — Опыт работы с фреймворками: LangChain, LangGraph, Mastra, CrewAI, Autogen, HuggingFace, OpenAI, Claude, Mistral. — Знание CI/CD, контейнеров, облачных решений, масштабирования, безопасности, observability (OpenTelemetry, Langfuse, Prometheus, Datadog), eval pipelines для AI. — Софт скиллы: автономность, системность, лидерские качества, навык приоритизации, ориентация на результат. — Русский — родной, английский — B2/C1 (большое преимущество). Условия: — Полностью удалённая работа. — Возможность влиять на продукт и развивать инновационные AI-решения. — Работа в динамичной и профессиональной команде.

Location: Hong Kong, Hong Kong SAR; Singapore, Singapore OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa. Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity We are seeking a Principal Engineer with a deep expertise in autonomous AI agent architecture and deployment, to spearhead the design, development, and optimization of intelligent agent systems on our global crypto exchange platform. This is a senior role requiring a hands-on AI science background combined with strategic oversight, technical direction, and cross-functional collaboration. You will work at the cutting edge of AI and crypto, designing AI agents that can autonomously reason, act, and evolve in complex, real-time environments such as trading, security, compliance, and customer interaction. What You’ll Be Doing Lead the design, development, and lifecycle management of autonomous AI agents tailored to crypto exchange operations (e.g., customer assistant, trading bots, risk detection agents, fraud prevention agents, etc.). Architect multi-agent systems (MAS) and frameworks to support goal-directed, reasoning, planning, and reinforcement-learning-based agents. Collaborate with product, engineering, business, and operation teams to define agent roles and measurable KPIs. Drive experimentation, simulations, and continuous learning pipelines for agent performance optimization. Stay ahead of the curve on developments in AGI, LLM-integrated agents, and cognitive architectures; apply relevant innovations to production systems. Mentor and lead a team of AI scientists and engineers; help define the AI roadmap and research strategy. Ensure system scalability, security, and regulatory compliance of AI-powered solutions in the crypto exchange domain. What We Look For In You PhD or Master's in Computer Science, AI, Machine Learning, Robotics or related field with at least 10 years of industry experience . Proven experience building and deploying autonomous AI agents in live environments. Ideal candidates will have: Designed multi-agent frameworks or agent ecosystems (e.g., AutoGPT, OpenAgents, LangGraph, LangFuse, Dify, Coze, etc.). Experience with goal-oriented agents, context engineering, memory management, tool use, and RL-based decision making. Developed agents for real-time decision-making, such as trading, gaming, customer support, or operations. Track record of delivering end-to-end AI systems in fast-paced, high-scale environments (preferably finance, crypto, or high-frequency domains). Nice to Haves Experience in top-tier AI labs or Tech industry leaders Experience in high-frequency trading, fintech, Web3, or crypto platforms is a strong advantage Startups or research-intensive environments preferred; must be comfortable operating with ambiguity and speed Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .