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Location: Remote, Canada; Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Partner Solutions Manager, Strategic Partnerships, you'll own the technical strategy and validation of joint work with GitLab's strategic partners: hyperscalers, artificial intelligence (AI) labs, AI-native companies, and development, security, and operations (DevSecOps) vendors. You'll work with partners, Product, and Research and Development (R&D) to turn partner and customer needs into integrations, influence product design, and build the business case for each joint effort, from the first architecture review to a shipped integration. In this role you will also own the AI model evaluation process with our model partners, working closely with our R&D teams to assess new models ahead of release and ensure GitLab is built on the best-performing models for our customers. Unlike a customer-facing Solutions Architect role focused on adoption, you'll work with partners to make GitLab better. This role has no sales quota or variable pay. At GitLab, our mission is simple: to enable everyone to contribute to and co-create the software that powers our world. Our platform doesn't just bring people together-it unites teams and organizations, breaking down barriers and redefining what's possible in software development. What You’ll Do Own the end-to-end technical strategy for assigned strategic partners, connecting technical requirements with joint business objectives. Turn partner technology and roadmaps into integration, architecture, and product opportunities across the GitLab platform, from planning to monitoring and AI agents and model integrations. Resolve complex technical issues across GitLab and partner systems, including cloud architecture, tool integrations, and AI model and agent evaluation and integration. Build working prototypes to validate concepts with partners. Manage the end-to-end AI model evaluation process with our model partners, coordinating with R&D to secure early access, align on evaluation criteria and timelines, and translate results into partnership and product roadmap decisions. Design and facilitate partner workshops, architecture reviews, and cloud financial management (FinOps) sessions, bringing in the right participants and documenting outcomes and next steps. Build business cases for joint technical efforts, defining expected gains, costs, and resources, and securing agreement from partner managers, R&D, and leadership. Bring partner requirements and technical feedback into Product and R&D discussions to influence product design and the roadmap. Coordinate Engineering, Product, FinOps, Finance, Sales, and Marketing with partner technical teams to move joint work from evaluation to delivery. Track cloud, AI, and DevSecOps developments to advise partnership investment, and share reusable findings through documentation, the handbook, reference architectures, and technical content. What You’ll Bring Experience as a Senior Solutions Architect, Partner Solutions Architect, Partner Engineer, or in a similar senior technical role. Experience with technology partners or alliances is strongly preferred. An end-to-end understanding of the software development lifecycle, continuous integration and continuous delivery (CI/CD), and DevSecOps, with the ability to use GitLab in depth. Hands-on cloud architecture experience with at least one hyperscaler: Google Cloud, Amazon Web Services (AWS), or Microsoft Azure. Cloud cost or FinOps experience is a plus. Working knowledge of AI model evaluation frameworks and agent technology, including large language model (LLM) application programming interfaces (APIs), agent frameworks, and Model Context Protocol (MCP), and how they fit into developer workflows. Experience designing and facilitating technical workshops with senior technical and business stakeholders, and building business cases that scope work, estimate gains and costs, and size resources. An autonomous, ownership-driven approach: you identify work and move it forward without waiting for a project backlog. Clear, concise written and spoken communication, adapting technical and commercial trade-offs for audiences from engineers to executives. Ability to travel about once a quarter and work hours that overlap with Europe, the Middle East, and Africa (EMEA). If you're on the US West Coast, expect to start around 7 a.m. Pacific Time; when the day ends is flexible. About the team You'll join Strategic Partnerships and report to the VP, Corporate Development and Strategic Partnerships. The team manages GitLab's relationships with hyperscalers, AI companies, and select DevSecOps companies, acting as the business owner and coordinating work across hosting contracts, product integrations, go-to-market activities, purchasing, and partner-specific marketing. Partner managers and directors lead each relationship; you'll own the technical side across partner types and collaborate with R&D, Engineering, FinOps, Finance, Sales, and Marketing. The role is remote, with Eastern Time preferred and other US time zones possible. How GitLab will support you Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental leave Home office support Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $115,200 — $194,400 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

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

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

Location: New Jersey; New York; Connecticut Workplace: remote About the role: At Provectus, you don’t prototype in a sandbox and hand a client a slide deck. You embed inside a bank, an insurer, or a health system, learn the work, and ship a Claude + AWS system into their real, regulated production — then you watch the business number move. We’re an Anthropic and AWS Partner with 150+ certified architects. That means the newest models, real budgets, and problems that matter — without the layers, politics, and years-to-impact of a hundred-thousand-person firm. 350+ builders, 100+ customers in production, founder-led since 2010, offices across North America, LATAM, and EMEA. Why Provectus Frontier access — Anthropic Preferred Partner: newest Claude models, real budgets, real problems. Real tasks (not scope), real deployment. You go where the operating problem is and ship production code to real users, not slide decks that sit in a drive. Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Embedded is not staff-augmented. Autonomous. You own the work from framing through to production, visible to clients and founders.  You own the method; nobody hands you a ticket. Your experience stays portable. Every engagement is a new problem, often in a new industry, so your skills compound instead of narrowing to one product. No legacy-maintenance grind, not a single stack. Craft & growth — build beside senior FDEs on a Claude-certified team; a real platform (talks, OSS) to grow your name. We give you a stage: Claude certification, conference talks, open-source contributions, published work under your own name. Requirements: Typically 7+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way. Genuinely willing to spend time understanding someone else’s job before writing code; demonstrated ability to become conversant in an unfamiliar business function quickly. Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works. You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why. Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Cloud-native delivery on AWS (GCP/Azure a plus), including the operational reality of a system someone else inherits. Pre-sales experience. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room. Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job. Hands-on experience with Claude, Claude Code, and Cowork (skills, workflows, MCP, integrations, connectors, custom harnesses). Nice to have Containers and orchestration (Kubernetes/ECS) and CI/CD in production. IaC depth: AWS CDK, CloudFormation, Terraform. Open-source contributions or public writing on applied AI. What you’ll do: Spend the first weeks of each engagement in the operator’s seat, learning the constraints from the inside, then rebuild the function from first principles alongside the client and a Forward Deployed Executive — and you are the one who builds it. Be part of the customer’s team, inside their process; work on real tasks, not scoped deliverables, and own the method autonomously. You won’t start from zero. Provectus builds industry Blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it. Deliver across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare. Help shape how leading enterprises adopt AI, from strategy through first deployment; help build the tooling and frameworks of a growing AI delivery practice. What We Offer: Full-time. On-site visits to client’s facilities and our office in NYC 1/week Unlimited Vacation policy. Generous health, vision, and dental insurance. 401(K) matching plan. Base salary $200k–$250k + bonuses; OTE up to $300k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, and abilities of the applicant, and alignment with market data. No Visa sponsorship We’re real: A person reads your application. We filter out AI-generated and fraudulent submissions automatically. Every application that clears that filter is read by someone on our team. We may ask for a quick ID verification. Fraudulent applications are common, and this protects real candidates. You’ll meet the people you’d work with. We host AI engineering events in New York and San Francisco. We'd be happy to talk in person. Your interviews are with Pavel Borobov, Solutions Architect, and Stepan Pushkarev, Co-Founder & CEO. How we hire: Recruitment interview. Tech interview with our FDE (30 min). A live engineering building session (1.5 hours). We hand you a business function and the constraints. You have to understand the job and rebuild it — then say what you’d build and how you’d know it worked. Use AI the way you normally would; we expect AI-generated code. What we look at is how you used AI, the judgment behind your choices, and what you researched beyond what any LLM already knows. 4. Soft skills interview. 5. Offer discussion.

— Тестировать продукт целиком: мобильные приложения, веб, API, взаимодействие сервисов между собой. — Проверять не только то, что описано в acceptance criteria, а систему в целом: граничные случаи, сбои интеграций, поведение при плохой сети и параллельных действиях.

— Manage IT subscriptions & equipment lifecycle; — Config macOS/Windows via MDM (Intune, ABM);

3+ года коммерческой разработки на Python (FastAPI / Django). Опыт с PostgreSQL, ORM, REST API и вебхуками.

-AI Engineer должен находить участки, которые сегодня требуют человека, таблиц, браузера и десятков повторяющихся действий, и превращать их в автоматизированные процессы, сервисы или AI-агентов. -Python и SQL/PostgreSQL.

What you’ll do Run and grow the live games. Take ownership of current live titles from day one: monitor CCU, retention (D1/D7/D30), session length, conversion and ARPDAU, and decide what to update, rebalance or sunset.

Задачи: — Поддержка текущего сайта: обновление, редизайн, создание новых страниц; — Создание креативов; — Проектирование прототипов и интерактивных концептов; — Анализ пользовательского опыта и предложение UX-улучшений; — Контроль качества дизайна на этапе реализации; — Периодическое участие в работе над интерфейсами продукта и дизайн-системой. Требования: — Сильный опыт работы с сайтами и лендингами; — Владение актуальными инструментами в Figma: variables, libraries, slots, grid, motion, agent; — Хорошие навыки композиции, типографики, работы с сетками и визуальной иерархией; — Умение создавать сильный графический контент и креативы; — Активное использование AI в работе: для концептов, графики, прототипов и других дизайн-задач; — Понимание no-code и vibe coding подходов или желание быстро в них разобраться; — Внимание к деталям: аккуратные макеты, порядок в слоях и компонентах, pixel perfect; — Самостоятельность и инициативность: умение предлагать решения, аргументировать их и доводить задачи до результата. Условия: — $1,500–2,500 в месяц в зависимости от опыта, результатов интервью и тестового задания; — Гибкий график работы из любой точки мира с плавающим началом рабочего дня; — Формат без бюрократии и микроменеджмента; — Подписки на AI-сервисы, графические, аудио и фотостоки для продуктивной работы; — Возможность работать не только с сайтом, но и при желании подключаться к дизайну самого продукта или коммуникационному дизайну. Будет преимуществом: — Опыт сборки сайтов во Framer; — Опыт работы с AI-агентами и vibe coding; — Знание MCP, skills, agent window (или IDE); — Photoshop на среднем или высоком уровне; — Сильные навыки графического дизайна, усиливающие визуальную составляющую интерфейсов; — Наличие в портфолио лендингов, креативов, веб-платформ.

Location: San Francisco, CA; Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker is looking for a Senior Software Engineer to build AI-powered developer tools and the platform behind them on our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building tools that make Docker's engineers more productive and the platform that lets teams across Docker prototype, deploy, and scale their own AI developer tools. As a Senior Engineer you will design, build, and operate: - AI-Powered Developer Tools. AI agents and tools that accelerate developer productivity, surface observability insights, and automate operational reviews. You will help teams adopt an AI-native way of working and speed up adoption of tools such as Claude Code and Codex. Examples include AI-powered code review and refactoring assistants, automated test generators and local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call work during incidents. - Self-Service AI Developer Tools Platform. Platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools, using the patterns and standards the team sets. You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal and Staff engineers, product engineering teams, and platform teams across Docker. _______________________________________________________________________ WHAT WOULD MAKE SOMEONE SUCCESSFUL IN THIS ROLE You are a strong, hands-on engineer who enjoys building at the intersection of AI, developer experience, and platform engineering. You have built software on top of LLMs and AI agents and have a good sense of what works in practice. You care about the people using your tools, and you can tell a tool developers love from one they put up with. You take a feature from a vague request to production: you ask the right questions about requirements, design it sensibly, ship it iteratively, and operate it afterward. You are comfortable in a fast-moving AI landscape and balance quality with pragmatism. You make the engineers around you better through thoughtful code review and by sharing what you learn. _______________________________________________________________________ RESPONSIBILITIES BUILDING AND OPERATING AI DEVELOPER TOOLS - Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation - Integrate LLM providers, agent orchestration frameworks, and MCP servers into reliable tools, following the team's patterns and standards - Build systems that help Docker's engineers adopt AI-native development tools - Build integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems - Instrument your tools so the team can measure adoption, effectiveness, and developer satisfaction SELF-SERVICE PLATFORM - Build and improve platform capabilities, including deployment pipelines (ArgoCD/GitOps), observability integration (Grafana), security controls, and cost management, that help teams ship their own AI tools faster - Help reduce time-to-production for new products from weeks to days - Contribute to reliability, security, and performance standards for AI systems, including SLOs, monitoring, and incident response DESIGN AND DELIVERY - Take features and services from requirements to production. Ask about scale, users, and constraints before designing, and write down your assumptions - Write clear design documents for the work you own, and participate in design reviews for the team - Review code thoroughly and share what you learn with the rest of the team - Take part in the team's on-call rotation, and help improve alerts, runbooks, and tooling so the next incident is easier - Follow the AI-assisted engineering practices the team sets: use AI tools critically, validate their output, and stay accountable for what you ship COLLABORATION AND GROWTH - Work with the Senior Manager and the team's Principal and Staff engineers to understand priorities and shape your work - Partner with Infrastructure, Security, and Data teams on integrations and shared standards - Help newer engineers get up to speed on the team's tools, patterns, and practices - Keep up with emerging AI/LLM technologies and agent frameworks, and bring useful findings back to the team _______________________________________________________________________ QUALIFICATIONS REQUIRED - 6+ years of software engineering experience building and operating production systems - Hands-on experience building LLM-powered applications, AI agents, or AI-assisted developer tools - Working knowledge of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows - Experience building and operating scalable backend services, distributed systems, or developer-facing platforms - Experience with cloud-native infrastructure, including Kubernetes, CI/CD pipelines, and observability systems - Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals - Experience building developer tools, platform systems, or internal tools that other engineers rely on - Good product judgment: you weigh developer experience and technical trade-offs, and you ask clarifying questions before committing to a design - Clear written and spoken communication suited to a remote, async-first company - Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments - Comfortable working autonomously in a distributed, remote-first team - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience PREFERRED - Experience with MCP (Model Context Protocol) or similar AI agent integration standards - Background in developer productivity, DevOps, SRE, or platform engineering - Contributions to open source AI tools, developer tooling, or platform engineering projects - Experience with GitOps deployment patterns (ArgoCD) or Grafana-based observability - Familiarity with security and operational practices for production AI systems - Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure) _______________________________________________________________________ WHAT TO EXPECT FIRST 30 DAYS - Learn Docker's current AI and developer tooling landscape, the team's services, and the architecture decisions made so far - Meet your teammates, including the Principal and Staff engineers, the Senior Manager, and key stakeholders in product engineering - Ship a useful production change in your first weeks, using the team's AI-assisted engineering workflow - Review existing AI tool prototypes and internal experiments to learn what is working and what is not - Shadow on-call and learn the operational profile of the team's services FIRST 90 DAYS - Own a feature or service from design to production with measurable impact, such as part of an AI code review agent, a deployment diagnostic agent, or an on-call assistant - Write the design document for work you own and take part in team design reviews - Contribute to the self-service platform's deployment pipeline, observability integration, or security controls - Take part in the on-call rotation FIRST YEAR OUTLOOK - Be a go-to engineer for one or more of the team's AI tools or platform components - Have shipped multiple production improvements that Docker engineers use every day, with adoption and effectiveness you can point to - Have helped teams outside AI Developer Tools build and run their own tools on the platform - Have taken on larger and more ambiguous work as your scope grows, and helped newer engineers do the same Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________   COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. United States: $160,900 – $260,700 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: San Francisco, CA; Seattle, WA Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ THE ROLE Docker is looking for a Staff Software Engineer to set the technical direction for key parts of our AI Developer Tools team. You will work at the intersection of AI, developer experience, and platform engineering, building the AI-powered tools and the platform that let teams across Docker prototype, deploy, and scale their own AI developer tools. As a Staff Engineer you will own the technical design and delivery for two areas: - AI-Powered Developer Tools. Design and build AI agents and tools that accelerate developer productivity, surface observability insights, and automate operational reviews. You will help teams adopt an AI-native way of working and speed up adoption of tools such as Claude Code and Codex. Examples include AI-powered code review and refactoring assistants, automated test generators and local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call work during incidents. - Self-Service AI Developer Tools Platform. Design and build the platform infrastructure that lets product and platform teams across Docker unblock themselves by prototyping, building, and deploying their own AI developer tools. You will set the patterns and standards that let teams experiment, iterate quickly, and graduate successful prototypes into production-ready services. You will report to the Senior Manager, AI Developer Tools, and work closely with the team's Principal engineers, engineering leadership, product engineering teams, and platform teams across Docker. _______________________________________________________________________ WHAT WOULD MAKE SOMEONE SUCCESSFUL IN THIS ROLE You are a hands-on technical leader who works well at the intersection of AI, developer experience, and platform engineering. You have shipped production systems built on LLMs and AI agents, and you can explain what worked and what did not. You think in platforms: you design something once so that several teams can move faster. You have clear opinions about what makes a developer tool good, and sound judgment about when to build, when to integrate, and when not to do either. You are comfortable in a fast-moving AI landscape and balance technical quality with shipping iteratively. Before you design, you ask about scale, users, and constraints rather than assuming them. You lead through influence and mentorship, and the engineers around you get better because you are on the team. _______________________________________________________________________ RESPONSIBILITIES TECHNICAL LEADERSHIP AND ARCHITECTURE - Set technical direction and own the architecture for AI-powered developer tools and the self-service platform within the team's roadmap - Turn ambiguous problems into clear requirements, plans, and staged decisions. Ask about scale, users, and constraints before designing, and write down your assumptions - Establish patterns, standards, and best practices for LLM integration, agent development, and production AI systems that the team and partner teams adopt - Shape platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools - Design reliable, scalable infrastructure for hosting AI agents and developer tools - Recommend AI technology choices, LLM provider strategies, and agent orchestration approaches, and make the trade-offs explicit - Work with the Senior Manager and the team's Principal engineers to align technical decisions with business goals SYSTEMS DESIGN AND IMPLEMENTATION - Design, build, and operate production AI agents for developer productivity, including code review assistants, test generators, deployment diagnostics, and incident response automation - Build self-service platform capabilities that reduce time-to-production for new AI tools from weeks to days - Build systems that speed up adoption of AI-native development tools (Claude Code, Codex) across Docker's engineering organization - Set and meet reliability, security, and performance standards for AI systems, including SLOs, monitoring, incident response, and cost management - Design integrations between AI developer tools and existing developer infrastructure such as CI/CD pipelines, observability platforms, and deployment systems - Take part in the team's on-call rotation and use what you learn to improve alerts, runbooks, and tooling STRATEGIC IMPACT AND INNOVATION - Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks, and recommend what Docker should adopt and what it should not build - Define how we measure AI tool effectiveness, including adoption, productivity gains, and developer satisfaction - Improve AI tool performance, cost efficiency, and developer experience through architectural improvements - Contribute to the technical approach for hardening proven internal tools so other teams across Docker can adopt them - Lead cross-team technical delivery, surface risks early, and work with Infrastructure, Security, and Data on shared standards LEADERSHIP AND MENTORSHIP - Mentor senior engineers on AI/LLM integration patterns, agent development, and platform engineering practices - Lead design reviews and raise the quality of design and code reviews across the team - Create reusable patterns, documentation, and architectural decision records (ADRs) that other engineers build on - Help the team turn good individual AI-assisted engineering practice into shared workflows and controls - This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed. _______________________________________________________________________ QUALIFICATIONS REQUIRED - 8+ years of software engineering experience, with demonstrated technical leadership on production systems at scale - Hands-on production experience building LLM-powered applications, AI agents, or AI-assisted developer tools - Strong understanding of LLM APIs (OpenAI, Anthropic, and others), prompt engineering, agent orchestration frameworks, and practical uses of AI in software development workflows - Track record of designing and building scalable distributed systems and developer-facing platforms - Production experience with cloud-native infrastructure, including Kubernetes, GitOps deployment patterns, observability systems, and CI/CD pipelines - Proficiency in Go (preferred), Rust, Java, or Python, with strong software engineering fundamentals - Experience designing developer tools, platform systems, or internal tools that other teams rely on - Track record of clarifying requirements and constraints with stakeholders before committing to a design - Track record of leading complex initiatives that crossed team boundaries, and of influencing without direct authority - Strong product and platform judgment: you weigh business outcomes, developer experience, and technical trade-offs - Clear written and spoken communication suited to a remote, async-first company - Track record of mentoring other experienced engineers - Ownership mentality with a bias for action and iterative delivery in ambiguous, fast-moving environments - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience PREFERRED - Experience with MCP (Model Context Protocol) or similar AI agent integration standards - Background in developer productivity, DevOps, SRE, or platform engineering - Contributions to open source AI tools, developer tooling, or platform engineering projects - Experience evolving an internal platform or tool into a widely adopted, production-grade service - Knowledge of security, compliance, and operational practices for production AI systems - Experience with infrastructure-as-code (Terraform, Pulumi) and major cloud platforms (AWS, GCP, Azure) _______________________________________________________________________ WHAT TO EXPECT FIRST 30 DAYS - Build context on Docker's current AI and developer tooling landscape, including existing AI experiments, LLM provider relationships, and architecture decisions to date - Meet the Senior Manager, the team's Principal engineers, engineering leadership, and key stakeholders in product engineering to understand needs, pain points, and constraints - Review existing AI tool prototypes and internal experiments to learn what is working and what is not - Get hands-on with the team's services and ship a useful production change - Shadow on-call and learn the operational profile of the team's services - Work with the Senior Manager to agree on initial technical priorities FIRST 90 DAYS - Lead the technical framing and delivery plan for one significant problem, from requirements through to production - Document the architecture, technology choices, and integration patterns for that work, including ADRs - Ship a production AI developer tool that sets patterns the team can reuse (for example an AI-powered code review agent, a deployment diagnostic agent, or an on-call assistant) - Contribute to the technical foundations of the self-service platform: deployment pipeline, observability integration, security controls, and cost management - Lead design reviews for your area of the roadmap and begin mentoring other engineers - Take part in the on-call rotation FIRST YEAR OUTLOOK - Be the trusted technical lead for your area of AI Developer Tools, and a sought-after partner for teams across Docker - Deliver at least one material outcome that spans multiple teams or systems - Have multiple production AI tools demonstrating value across Docker's engineering organization - Help build a self-service platform that lets multiple teams build, deploy, and operate their own AI tools with little friction - Define and instrument how the team measures AI tool adoption, effectiveness, and developer productivity impact - Grow other senior engineers into broader ownership, and reduce the number of decisions that depend on a small set of people Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________   COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location. United States: $170,350 – $275,550 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Задачи: — Участие в проектах заказной разработки и пресейлах, включая направление искусственного интеллекта. — Полный цикл аналитических работ: от сбора и формализации требований до функционального тестирования, приёмки и демонстрации решений заказчику. — Ежедневное использование LLM-ассистентов (YandexGPT, GigaChat и аналогов) для решения рабочих задач. Требования: 1. Анализ и проектирование — Моделирование бизнес-процессов: BPMN 2.0. — Проектирование систем: UML (диаграммы классов, последовательностей, активности), Use Case. — Сбор требований: User Story, Use Case, техники интервьюирования. — Проектирование баз данных: ER-модели, нормализация, понимание транзакционности. — Прототипирование интерфейсов: Figma (базовая работа с макетами), Miro (схемы, майндмэпы). 2. Работа с данными и API — Уверенное владение SQL (PostgreSQL, MySQL): сложные JOIN, оконные функции, CTE, оптимизация запросов. — Работа с форматами данных: JSON, XML, YAML. — Интеграции: REST API, SOAP. — Понимание принципов работы брокеров сообщений на уровне producer/consumer. — OpenAPI (Swagger): чтение спецификаций, внесение изменений. 3. Инструменты и процессы — Jira: создание и декомпозиция задач, ведение спринтов. — Confluence: оформление ТЗ, архитектурных решений, ведение базы знаний. — Методологии разработки: Agile (Scrum, Kanban), понимание Waterfall. — Git: ветвление, merge/pull requests в GitLab или GitHub. — Postman: коллекции, переменные, скрипты для тестирования API. 4. Искусственный интеллект и векторные данные — Ежедневное применение LLM-ассистентов (YandexGPT, GigaChat и аналоги). — Использование ИИ для написания SQL-запросов, генерации и рефакторинга кода. — Применение LLM для подготовки документации, анализа требований, проектирования API и моделей данных. — Понимание принципов работы векторных баз данных. — Опыт работы с Qdrant, pgvector или аналогами: хранение эмбеддингов, базовый векторный поиск. 5. Личностные качества — Проактивность и самостоятельное доведение задач до результата. — Умение принимать решения при неполных вводных. — Способность быстро погружаться в новую предметную область. — Стрессоустойчивость при сжатых сроках и параллельной загрузке. Дополнительные требования (будет плюсом): 1. Разработка ИИ-решений — LangChain, LangGraph: построение цепочек (chains), агентов и RAG-пайплайнов. — Понимание протоколов MCP (Model Context Protocol) и A2A (Agent-to-Agent). 2. Обработка неструктурированных данных — Интеграция SpeechKit, Whisper или аналогов для распознавания речи. — Работа с Tesseract, Yandex Vision или аналогами для OCR и извлечения данных из документов. 3. Инфраструктура и архитектура — Архитектурное моделирование: C4 Model. — Docker, Kubernetes: понимание YAML-конфигураций, Helm. — Yandex Cloud, AWS: S3/Object Storage, Managed Databases, IAM. — RabbitMQ, Kafka: настройка, топики, партиции, гарантии доставки. 4. Разработка и хранение данных — Python для написания ETL/ELT-скриптов, работы с Pandas и автоматизации. — Опыт построения DWH. — Моделирование данных: Data Vault, Star Schema, Snowflake Schema. — Работа с BI-инструментами.

Задачи: — Техническое руководство продуктом и командой из трёх инженеров. — Ответственность за архитектуру, качество и стоимость агентов в продакшене. — Планирование работ и найм сотрудников. — Разработка ключевых компонентов и ревью кода команды. — Работа с ядром платформы на Python: сессии, ходы агента, политики заказчика, учёт. — Обеспечение агентного цикла под бюджет хода. — Перевод пользователей с пилотной архитектуры на целевую без остановки работы. — Снижение стоимости хода через оптимизацию состава входа, кэш префикса, лимитов инструментов, роутинга моделей, учёта по пользователю и ходу. — Оценка качества с использованием эталонов из реальных трасс, сравнение моделей, регрессия на каждый промпт, инструмент, модель. — Работа с данными компании через MCP с правами пользователя: 1С, КХД, СЭД, HR, поиск по документам с ACL. — Обеспечение надёжных фоновых процедур и записи во внешние системы. — Создание песочницы для кода агента без секретов и исходящих соединений. — Декомпозиция задач, ревью, работа с ИИ-агентами для кода, найм. — Согласование требований и информационной безопасности с заказчиком. Требования: — Опыт backend-разработки от 7 лет, из них минимум 2 года на позиции техлида или ведущего инженера. — Ответственность за продакшн. — Опыт работы с Python в продакшене: asyncio, FastAPI, Pydantic, типизация, тесты. — Умение читать и править код на Go. — Опыт работы с LLM-агентами в продакшене с реальными пользователями: tool calling, цикл агента, ограничения, обработка ошибок. — Понимание контекста и стоимости вызовов, кэш префикса, сжатие истории, лимиты инструментов, расчёт цены хода по usage. — Опыт с Eval: эталонные наборы, LLM-судья и его проверка, регресс, трассировка (Langfuse, OpenTelemetry). — Знания гибридного поиска, реранкинга, чанкинга, прав доступа к документам. — Опыт работы с MCP, OAuth 2.0/OIDC, token exchange. — Знание durable execution (Temporal, Restate или аналог), идемпотентность, повторы. — Опыт работы с PostgreSQL, Redis, S3, Docker, Kubernetes, GitLab CI. — Опыт работы с открытыми моделями через OpenAI-совместимые API, LiteLLM или аналог. — Высшее техническое образование. Условия: — Возможность работать из любой точки мира. — ИТ аккредитация. — Программа корпоративных привилегий BestBenifit. — ДМС со стоматологией. — Доступ к обучению английскому языку. — Возможность проходить курсы и тренинги по развитию в сфере вашей специальности. — Участие в интересных и сложных проектах в области искусственного интеллекта. В сопроводительном письме необходимо указать опыт управления командой, образование и используемые технологии ИИ в разработке.

Задачи: — Выстроить конвейер внедрения AI-решений: от заявки до поддержки; — Развивать Doc AI и автоматизацию обработки документов; — Внедрять AI-агентов, MCP-коннекторы и автоматизацию бизнес-процессов; — Управлять командой AI-инженеров; — Контролировать качество решений, метрики, SLA и ROI; — Обеспечивать внедрение LLM-решений. Требования: — практический опыт с LLM, RAG и AI-агентами от 3 лет, опыт управления командой и внедрения AI-решений. Условия: — Совокупный доход: оклад + годовой бонус.

Задачи: — Работа с ядром платформы на Python: сессии, ходы агента, политики заказчика, учёт; агентный цикл под бюджет хода. — Перевод пользователей с пилотной архитектуры на целевую без остановки работы. — Снижение стоимости хода: состав входа, кэш префикса, лимиты инструментов, роутинг моделей, учёт по пользователю и ходу. — Оценка качества: эталон из реальных трасс, сравнение российских моделей, регресс на каждый промпт, инструмент, модель. — Работа с данными компании через MCP с правами пользователя: 1С, КХД, СЭД, HR, поиск по документам с ACL. — Надёжные фоновые процедуры и запись во внешние системы: расписания, подтверждение человеком, ровно одна запись. — Песочница для кода агента: на сессию, без секретов и исходящих соединений. — Команда: декомпозиция, ревью, работа с ИИ-агентами для кода, найм. — Согласование требований и ИБ с заказчиком. Требования: — Опыт backend-разработки от 7 лет, из них от 2 лет — техлид/ведущий инженер с ответственностью за продакшн. — Опыт работы с Python в продакшене: asyncio, FastAPI, Pydantic, типизация, тесты. — Умение читать и править код на Go. — Опыт работы с LLM-агентами в продакшене с реальными пользователями: tool calling, цикл агента, ограничения, обработка ошибок. — Понимание контекста и стоимости: вход вызова, кэш префикса, сжатие истории, лимиты инструментов, расчёт цены хода по usage. — Опыт с Eval: эталонные наборы, LLM-судья и его проверка, регресс, трассировка (Langfuse, OpenTelemetry). — Знание гибридного поиска, реранкинга, чанкинга, прав доступа к документам. — Опыт работы с MCP, OAuth 2.0/OIDC, token exchange — агент от имени пользователя. — Знание Durable execution (Temporal, Restate или аналог), идемпотентность, повторы. — Опыт работы с PostgreSQL, Redis, S3, Docker, Kubernetes, GitLab CI. — Опыт работы с открытыми моделями через OpenAI-совместимые API, LiteLLM или аналог. — Высшее техническое образование. Условия: — Полная занятость. — График работы 5/2. — Формат работы: удалённо или гибрид.

Задачи: — Владение стратегией MCP и AI экосистемы, определение функций и интеграций, а также значения MCP и AI для Lattice и клиентов. — Стать лидером мнений в области влияния AI на HR. — Выпуск интеграций, создающих рычаги влияния. — Разработка новых инструментов MCP, ресурсов и коннекторов третьих сторон для расширения возможностей администраторов, менеджеров и руководителей с данными Lattice. — Создание прочных основ, таких как аутентификация, агентные фреймворки исполнения и оценки, для ускорения и упрощения будущих интеграций. — Техническое партнерство с инженерной командой, помощь в формировании технического направления дизайна инструментов MCP и инфраструктуры интеграций. — Ведение выхода на рынок совместно с партнерами, запуск интеграций с измеримым влиянием. — Влияние и владение ключевыми результатами по интеграциям, принятию MCP, надежности, активации и росту экосистемы. Требования: — Опыт управления продуктом не менее 6 лет с значительной ответственностью за интеграции, платформы или экосистемы (созданные с нуля, а не просто настроенные). — Прямой опыт создания технических интеграций в мультисторонних экосистемах, например, в ad tech, marketing tech, маркетплейсах, платформах для разработчиков, платежах, подключении данных и т.п., где стратегия партнерства формировала продукт. — Сильная техническая грамотность: уверенное владение паттернами интеграций третьих сторон, синхронизацией данных, сопоставлением полей, вебхуками, аутентификацией/OAuth. — Умение вести архитектурные дискуссии с инженерами и CTO партнеров. — Опыт выпуска масштабируемых и надежных интеграций в сотрудничестве с инженерной командой и организации запусков с go-to-market командами. — Доказательства превращения платформы или экосистемы в конкурентное преимущество. — Четкая и уверенная коммуникация для согласования внутренних команд и внешних партнеров. — Склонность к действию и способность создавать структуру в неоднозначной и быстро меняющейся среде. Плюсы: — Опыт работы с AI экосистемами, MCP, AI агентами или инструментами LLM. — Опыт в мультипродуктовом B2B SaaS или с HR/персональными данными или другими чувствительными данными. Условия: — Полная занятость. — Удаленная работа из Канады, только для резидентов Британской Колумбии или Онтарио. — Годовая зарплата от 135000 до 180000 CAD. — Возможность участия в программе опционов. — Медицинское, стоматологическое и офтальмологическое страхование. — Страхование жизни и от несчастных случаев. — Краткосрочное и долгосрочное страхование по инвалидности. — Поддержка психического здоровья и благополучия. — Поддержка в чрезвычайных погодных условиях. — 16 недель оплачиваемого отпуска по уходу за ребенком. — Гибкий график отпусков, 13 праздничных дней и 3 дня восстановления. — Ежемесячный удаленный рабочий бонус и единовременный бонус на обустройство домашнего офиса. — Взносы в пенсионный план RRSP до 3% от валовой зарплаты (максимум 2500 CAD в год). — Возмещение расходов на обучение до 500 CAD в год. Компания: Lattice — платформа для успеха сотрудников, предлагающая инструменты для оценки эффективности, опросов вовлеченности, обратной связи, постановки целей и планирования карьеры, направленные на развитие и удержание сотрудников. Компания обслуживает более 5000 клиентов по всему миру, включая Loom, Robinhood и Gusto. Lattice придерживается политики равных возможностей и инклюзивности. Местоположение: Удаленная работа из Канады, только для резидентов провинций Британская Колумбия и Онтарио.

Задачи: — Работа с внутренней платформой IDP с живым контекстом — Ревью кода от AI-агентов Требования: — Опыт работы с MCP-серверами — Знание Trigger.dev — Опыт работы с OpenRouter — Понимание AI-безопасности, включая prompt injection, права инструментов и регрессионный eval Условия: — Работа в офисе в Лимассоле, Кипр — Полная занятость — Зарплата от 5000 до 6000 евро в месяц