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Location: North Macedonia; Bosnia and Herzegovina; Czechia; Croatia; Moldova; Bulgaria; Montenegro Workplace: remote Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDE and an FDX,  Forward Deployed Executives (FDX) own the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs. Forward Deployed Engineers (FDE) embed with a client, map the client workflow, identify the business problem underneath it, design and build a working AI solution, present to the client, and transfer the knowledge to the client's team. Owns technical direction of the whole solution. Senior AI Engineer. When an FDE comes back from the client with the business problem, you will help turn that into an agentic system that runs in production and will be responsible for evaluation, observability, and guardrails.  You'll have real ownership of components and of the technical decisions inside them. Requirements: Mindset Proactive and self-directed; you push for clarity rather than waiting for a ticket Excellent communication and problem-solving skills Comfort with ambiguity and ownership. B2+ English, comfortable collaborating across distributed, multicultural teams. Technical depth 5+ years in software or ML engineering, with production systems you were accountable for. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes. Shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks. Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production. Strong engineering fundamentals. Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure. Python and/or TypeScript proficiency; depth matters more than stack. Dropped into an unfamiliar codebase, you're productive. Hands-on AWS in production: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus. Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release. Model and agent monitoring, drift detection. Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs. Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus. MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus. Nice to Have: Experience in one of the industries: financial services, insurance, healthcare. Consulting, professional services, or other embedded customer-facing delivery. AWS and Claude Code Certifications A2A: you can explain agent-to-agent interoperability CI/CD pipeline experience (GitHub Actions, GitLab CI) Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Experience in an additional language (Go, TypeScript, or Rust). Experience with Apache Spark, Apache Airflow, Kafkа Responsibilities: Work in a pair with an FDE and an FDX. Build and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Build and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Integrate AI components into backend services and RESTful APIs Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD. Implement LLMOps and AgentOps practices: agent tracing, prompt and version management, cost and latency monitoring, regression testing, drift detection Start from the blueprint, contribute to enablement and handover: clear documentation, runbooks, and pairing with the client engineers who will inherit the system. Feed reusable components and lessons back into the Provectus Blueprints Participate in technical discussions and architectural decisions Conduct model evaluation, improve failure modes you find, optimize model performance, efficiency, and reliability Mentor junior and mid-level AI engineers, conduct code reviews and share knowledge across the team through documentation, presentations, and workshops. What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside FDE and FDX A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire: Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

🧩 Требования: уверенное знание C# и .NET, коммерческий опыт от 3 лет, опыт разработки backend‑сервисов на Core, владение REST API и gRPC, понимание принципов проектирования ПО и написания тестируемого кода; опыт работы с PostgreSQL, MongoDB, Redis/KeyDB, брокерами сообщений (Kafka или RabbitMQ), Docker и Kubernetes;

Задачи: — Развёртывать и сопровождать приложения совместно с командами разработки. — Поддерживать CI/CD‑процессы с использованием GitLab, Jenkins, Ansible, Docker Swarm, Kubernetes, Helm, Terraform, Yandex Cloud. — Управлять секретами и доступом через HashiCorp Vault, RBAC в Kubernetes и GitLab. — Настраивать политики безопасности в Kubernetes (PodSecurity, Network Policies, OPA/Gatekeeper). — Развивать инструменты контроля безопасности цепочки поставок ПО. — Участвовать в реагировании на инциденты информационной безопасности и R&D‑задачах. — Диагностировать проблемы, работать с системами мониторинга и диагностики (Zabbix, ELK, Mimir, Grafana, Loki). — Автоматизировать процессы сопровождения и поддерживать документацию. Требования: — Уверенные знания Linux, включая Astra Linux, в области администрирования и безопасности. — Высокий уровень владения Bash. — Понимание принципов авторизации и аутентификации. — Опыт работы с реляционными и нереляционными базами данных. — Опыт работы с GitLab CI. — Знание сетевых технологий (TCP/IP, модель OSI), принципов безопасности сетей, виртуализации и контейнеризации. — Понимание современных угроз информационной безопасности, методов и средств защиты информации. — Знание методологии оценки уязвимостей (CVSS, EPSS) и их классификации (CWE). — Опыт организации процессов разработки ПО (git, ветвление кода, CI/CD). — Опыт работы с Kafka и RabbitMQ. — Опыт работы с системами сбора логов (EFK/ELK, Grafana Loki) и мониторинга (Prometheus, Grafana). Плюсом: — Знание Golang или Python для автоматизации. — Опыт работы с Openstack. — Опыт работы в условиях импортозамещения.

в задачи входит разработка и поддержка DAG’ов в Apache Airflow, настройка CDC‑процессов для передачи данных в аналитическое хранилище, создание и оптимизация витрин данных на SQL, реализация трансформаций на Apache Flink, контроль качества данных инструментами Data Quality и оптимизация их обработки.

Location: Ukraine; Serbia; Yerevan; Spain; Slovakia; Slovenia; Poland Workplace: remote Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. What You’ll Do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. What You’ll Bring: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release Model and agent monitoring, drift detection Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) Experience in an additional language (Go, TypeScript, or Rust) Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire: Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: North Macedonia; Montenegro; Moldova; Bulgaria; Czechia; Bosnia and Herzegovina; Croatia Workplace: remote Provectus is an AWS Premier Partner and an Anthropic Strategic Partner , working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. What You’ll Do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. What You’ll Bring: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release Model and agent monitoring, drift detection Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus Nice to have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) Experience in an additional language (Go, TypeScript, or Rust) Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire: Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: Москва, удалённая работа, RU; Россия О команде Наша команда NoC развивает сетевую инфраструктуру, на которой держатся все сервисы компании. Мы работаем с масштабной и высоконагруженной сетью: от фабрик ЦОД и междатацентровых соединений до внешней связности и защиты от DDoS. У нас можно не только поддерживать работу сложной инфраструктуры, но и влиять на её архитектуру, внедрять новые технологии, тестировать оборудование и развивать сетевую автоматизацию вместе с командой NetDev. В основе нашего стека — Juniper, eBGP, MPLS, Linux и FRR. Здесь много нетривиальных инженерных задач, возможностей для профессионального роста и реального влияния на надёжность сервисов компании. Какие задачи вас ждут: поддерживать географически распределенную сеть; включающую сотни коммутаторов; участвовать в развитии существующей сетевой архитектуры; разрабатывать инструменты управления и автоматизации; участвовать в проектировании и строительстве сети нового ЦОДа: топология и ёмкость фабрики; адресация; DCI; стыки с действующими площадками; Готовить ADR; HLD/LLD и ТЗ на закупку; вести вендорную стратегию сетевого оборудования: требования; испытания; ввод второго вендора в продакшен; определять адресную политику и план по внедрению IPv6; оценивать долгосрочные риски сети (ёмкость; жизненный цикл оборудования; лицензии; поставки) и вести архитектурный техдолг; быть техническим лидом кросс-командных проектов: ИБ; Kubernetes; DBaaS; железо и ЦОД; команда разработки сетевой автоматизации; Ревьюить решения инженеров; развивать сильных инженеров команды. Мы ждём, что вы: хорошо разбираетесь в стеке TCP/IP и протоколе BGP; имеете опыт работы с оборудованием Juniper; Cisco или Huawei; понимаете; как работает сетевой стек Linux; обладаете опытом настройки и дебага; проектировали и участвовали в запуске крупной DC-фабрики (Clos; сотни и тысячи коммутаторов) или новый ЦОД целиком; имеете глубокие знания в маршрутизации и протоколе BGP: eBGP-фабрики; RR; политики; communities; траблшутинг масштабных сетей; понимаете принципы работы EVPN-VXLAN; Type-5 и L3VNI; в продакшене; MPLS L3VPN и стык VRF с L3VPN на DCI или border; сегментация с VRF и stateful-МСЭ в ЦОД: симметрия трафика; отказоустойчивость; расчёт мощности; работали с оборудованием Juniper (QFX; MX; PTX) на экспертном уровне; Опыт хотя бы с одним альтернативным вендором: Huawei; H3C; Cisco Nexus; Arista или SONiC; имеете представление о дизайне сетей на IPv6 и адресации для крупной инфраструктуры; умеете писать и защищать архитектурные решения (ADR; TDR; дизайн-документы) перед смежными командами и руководством; вёли кросс-командные проекты дольше квартала: организация людей; доведение до результата; подтверждение результатов метриками. Будет здорово, если вы: network-as-Code: генерация конфигурации из источника истины (NetBox), GitOps, NETCONF/gNMI. Python или Go на уровне чтения и ревью; опыт смены вендора или dual-vendor в продакшене; сетевая часть комплаенса: PCI DSS, аттестация сегментов; kubernetes-сети: Calico, BGP с подами, FRR на хостах; публичные выступления и статьи по сетям. Работа у нас — это: возможность реализовать идеи в проекте с многомиллионной аудиторией; высокая степень автономии и ответственности в принятии решений; мощное железо, AI-инструменты и всё, что нужно для продуктивной работы; прозрачная система премий, достойная зарплата — размер обсудим на собеседовании; личный бюджет на обучение, который можно тратить на книги, курсы и конференции; расширенная забота о здоровье: ДМС, страхование жизни, онкострахование; сервис психологической поддержки, а также финансовые, юридические и ЗОЖ‑консультации для вас и ваших близких; корпоративный спорт: групповые тренировки, велбиинг-приложение; спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; компенсация питания; замечательный офис в двух минутах от метро «Белорусская»: панорамный вид на центр города, места для уединённой работы и зоны отдыха.

Location: Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Warsaw, Poland; Yerevan, Armenia You’re the kind of person who looks at a complex, deeply interconnected system and immediately wants to know how it functions and what would make it break. Focused on finding more than just bugs, you want to untangle dependencies and raise the bar for quality across the board. You're comfortable stepping up to take ownership and lead the charge when things get complicated. About JetBrains We create intelligent software development tools for developers and teams. More than 15 million users, over 300,000 companies, and 88 of the Fortune Global Top 100 rely on our products to solve real, complex problems. Our mission is simple: make development teams more productive and AI adoptable at scale. What you’ll do DataGrip is our cross-platform IDE for databases, and its core functionality powers database tools across other JetBrains IDEs like IntelliJ IDEA, PyCharm, and PhpStorm. Because of the large size of this ecosystem, which spans many database systems, environments, and integrations, keeping our quality bar high is a complex, strategic challenge. You’ll take ownership of quality across these shared components and release processes. You’ll work closely with developers, DevOps, and product teams to map out expected behaviors, investigate hard-to-reproduce issues, and improve how we build and test. Your work will directly influence the reliability of tools used by millions of developers every day, making sure changes in one area don't unexpectedly break another. Day to day, you will: Lead QA activities, set priorities, and coordinate testing efforts across multiple teams and components. Investigate unclear, poorly reproducible, and cross-component issues to find the root cause. Analyze logs, test results, and user reports to spot broader quality patterns behind individual failures. Define test strategies and apply risk-based testing to manage release cycles safely. Work with developers and product teams to establish expected behavior when detailed specifications aren't available. Improve our QA processes and guide the team in making confident release decisions. What you’ll bring A high degree of independence and technical curiosity. Comfort taking ownership of complex problems and driving them to resolution. The ability to see the big picture and understand how changes in one area affect the rest of the system. A proactive approach to improving processes rather than just following them. Strong analytical thinking, with the ability to turn scattered logs and user reports into clear insights. The communication skills to bring different teams together and coordinate testing efforts smoothly. What you’ll need The proven ability to test complex, multi-component systems and navigate integration and end-to-end testing. Practical knowledge of relational databases and SQL. Experience leading QA activities and shaping test strategies for multiple products and release cycles. Familiarity with reading and debugging code (Java or Kotlin preferred) to troubleshoot deeply embedded issues. Hands-on experience writing or maintaining automated tests. The ability to navigate and troubleshoot CI/CD systems, test environments, and infrastructure (such as Docker, Kubernetes, or TeamCity). Experience investigating distributed systems, APIs, and service-to-service integrations using monitoring and logging tools. What success looks like Within your first few months, you’ll have a firm grasp of the DataGrip ecosystem and how its components interact across our IDEs. You’ll be independently identifying quality risks, setting clear testing priorities, and helping teams ship reliable releases without bottlenecks. Long-term, you’ll have established smoother QA workflows, making it easier for everyone to understand dependencies and catch cross-product issues before they reach our users. Why join JetBrains? Strong base salary. We offer competitive pay that reflects your skills and experience. Flexible work schedule. Enjoy the freedom to work from home or from the office. Extra time off. More days to relax, recharge, and do the things you love. Learning and development opportunities. Access to conferences, courses, and language classes. Relocation support. Assistance to help make your move as smooth and stress-free as possible. Free meals and snacks. Lunch and refreshments provided to keep you fueled. Health and wellness. On-site gyms or sports club stipends. Internal events. Company-wide celebrations and team gatherings. *Some benefits may vary depending on location. #LI-DNI We are an equal opportunity employer We know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation. We process the data provided in your job application in accordance with the Recruitment Privacy Policy.

• Руководить разработкой совместно с техническим лидом: работать с backlog, ставить задачи и контролировать их выполнение в срок. • Самостоятельно решать возникающие сложности и обеспечивать движение проекта.

🕹 Список обязанностей: — Определять и реализовывать стратегии инфраструктуры и кибербезопасности, технологические роадмапы, архитектурные стандарты, операционные процедуры и лучшие инженерные практики в соответствии с бизнес-целями банка.

• Разработка стратегии и целевой архитектуры мобильных и цифровых каналов банка. • Руководство, развитие и масштабирование команды мобильной разработки.

- Разработка серверной части государственных информационных систем в сфере здравоохранения; - Уверенное владение Java, понимание ООП, коллекций, обработки исключений и основ многопоточности;

• Разрабатывать релизы и инструменты поставки VK Private Cloud. • Ansible (roles/playbooks, Jinja2) + Python/Bash/Go.

💡 What you'll work on. • Backend development in Python.

Разработка и развитие компонентов ML-платформы и инфраструктурных сервисов. Автоматизация процессов запуска, сопровождения и мониторинга ML/HPC workloads.

– Опыт разработки на Java 17+; – Опыт применения Spring Boot (Spring Data, Spring Kafka);

Location: Remote Workplace: Remote More than 30,000 subscription apps run on Adapty: $5B in revenue tracked, 4B users served, 80B API calls a month. That's one of the richest datasets in the app economy. Our mission is simple: make apps rich. We are a team of 210+ passionate, product-driven people who love solving big challenges. Backed by top investors like 500 Startups and Surface Ventures, we are building one of mobile's fastest-growing SaaS companies and aiming for number one worldwide. Why this role matters Adapty runs on infrastructure we own and operate ourselves: a Proxmox and Ceph platform across OVH, Hetzner and bare metal, with parts of the stack in AWS and GCP. That platform processes billions of dollars of subscription volume a year, and today too much of it depends on a single engineer. You are joining to take that platform over: fully owned, fully described in code, and cheap to run at scale. In your first months you will own Proxmox and Ceph end to end, drive resource rightsizing, and lead the split of our development workloads into a separate cluster. What you'll do - Own our Proxmox and Ceph platform end to end — capacity, upgrades, performance and incident response - Run infrastructure across OVH, Hetzner and bare metal, alongside workloads in AWS and GCP - Drive resource rightsizing: find where we overpay, size it correctly, and prove the saving in the bills - Lead the separation of development workloads into a dedicated cluster, without downtime for production - Keep everything as code — Terraform, Ansible, ArgoCD and GitLab CI, with no manual changes in production - Own reliability: on-call, incident response, root cause analysis and the runbooks that follow - Support the data layer our product runs on — PostgreSQL, ClickHouse, Aerospike About you - 10+ years working with infrastructure and physical hardware, not only managed cloud - 5+ years running Kubernetes and cloud platforms in production - Hands-on with bare metal and providers like OVH and Hetzner, plus AWS and/or GCP - Strong with Terraform, Ansible, ArgoCD and GitLab CI — you describe infrastructure in code by default - Real FinOps experience: you can name what infrastructure cost before and after you touched it - You take problems all the way to the root cause and to the fix, including outside your formal scope - You say things directly — risks and bad news arrive early, not after the incident Nice to have - Production experience with Proxmox and Ceph specifically - PostgreSQL, ClickHouse or Aerospike at scale - Experience splitting shared environments into isolated clusters - Building observability and on-call practice from scratch - High-load B2B SaaS background WHAT'S IN IT FOR YOU - Strong product with industry-best metrics. Adapty is among the top 5% fastest-growing SaaS companies. - Exceptional compensation directly tied to your results. - Direct communication and ownership. No bureaucracy, no politics, just impact. - Flexible remote work. Join us from anywhere or work onsite in our Belgrade or London offices. - Professional support. Language learning, sports, health insurance, laptop coverage, and more.

Location: Москва, удалённая работа, RU; Россия О команде Наша команда развивает LLM Gateway — единую точку доступа в Авито к языковым моделям. Мы помогаем продуктовым командам быстро и безопасно получать доступ к подходящим моделям, прозрачно управлять лимитами, видеть стоимость каждого запроса и мониторить поведение моделей в продакшене. Какие задачи вас ждут: развивать платформу LLM Gateway для всего Авито, чтобы создавать продукты с LLM было быстро и удобно; автоматизировать выдачу и настройку доступов к моделям; развивать управление потреблением LLM: рассчитывать стоимость LLM-вызова; настраивать бюджетные лимиты для клиентов и приоритеты; развивать динамическую балансировку трафика между LLM-моделями; определять SLA платформы и методику его расчёта: настраивать мониторинг и алёрты; поддерживать выполнение SLA; интегрироваться с платформой инференса: автоматизировать развёртывание моделей; открывать доступ к моделям для пользователей в инфраструктуре Авито; взаимодействовать с DS-инженерами, погружаться в работу LLM, следить за развитием индустрии и внедрять новые технологии. Мы ждём, что вы: лидировали крупные стримы с высокой степенью неопределённости: собирали требования; планировали работу; декомпозировали задачи; отвечали за реализацию и мониторинг результатов; поддерживали критическую высоконагруженную инфраструктуру: настраивали мониторинг и алёрты; участвовали в дежурствах 24/7; программируете на Python или Go от 5 лет; имеете опыт в MLOps или LLMOps: поднимали и поддерживали инфраструктуру для автоматизации работы с ML- и LLM-моделями; понимаете базовые принципы работы LLM: чем prefill отличается от decode, зачем нужен KV-cache, что такое TTFT. Будет здорово, если вы: работали с LLM API и LLM Gateway или аналогичными решениями, например LiteLLM; создавали внутренние платформенные решения; работали с системами мониторинга LLM, например Langfuse или Opik; создавали и поддерживали административные интерфейсы для внутренних пользователей; работали с Kubernetes и GPU; работали с DWH, например ClickHouse или Trino. Работа у нас — это: расширенная забота о здоровье: ДМС, страхование жизни, онкострахование; сервис психологической поддержки, а также финансовые, юридические и ЗОЖ-консультации для вас и ваших близких; корпоративный спорт: групповые тренировки, велбиинг-приложение; спецпредложения от партнёров и скидки на сервисы Авито: бронирование жилья, услуги продвижения, ипотечное страхование; компенсация питания.