Назад до пошукуSenior Forward Deployed Engineer
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We are looking for a Forward Deployed Engineer to embed with enterprise clients, find their highest-value problems, and solve them with production-ready AI solutions. You will own each problem end-to-end: technical discovery, feasibility, architecture, build, deployment, and the measurable business outcome. You will work on the client side, learn the client's business in depth, and re-engineer its processes around AI on top of the platforms, data, and systems the client already runs.
This role combines hands-on AI and full-stack engineering, solution architecture, and technical consulting. You will work across AI models, agent frameworks, data and retrieval systems, cloud infrastructure, and enterprise integrations, using AI coding agents and agentic engineering practices to deliver fast. Depending on the engagement, your focus will lean toward one of three specializations: Partner Platform Expert, building on one AI partner's platform such as Anthropic, OpenAI, Google Gemini and Vertex AI, AWS Bedrock, or Microsoft Foundry; SDLC and AI Software Factory Expert, making client engineering teams measurably faster with harnesses and AI software factories; or AI Technical Solutions Expert, turning client use cases into production AI systems on the client's existing platforms. Success is measured by outcomes the client can see and sustain, not by features shipped or hours billed.
- Embed with client business and technical teams to identify high-value problems and own them end to end, from discovery and specification through architecture, build, production, and monitoring
- Map real business processes, including their exceptions, and decide which steps to automate, which to keep with a human in the loop, and which to leave to people
- Assess feasibility and recommend the most appropriate approach, whether agentic AI, deterministic workflows, traditional machine learning, or no AI at all
- Design end-to-end solution architectures covering models, orchestration, data and retrieval, enterprise integrations, identity, security, observability, and cost
- Personally build, test, and deploy production-grade, full-stack AI applications on clients' existing cloud and AI platforms
- Implement agent workflows, retrieval systems, tool integrations, evaluation frameworks, guardrails, monitoring, and human-in-the-loop controls
- Use AI coding agents and agentic engineering practices to accelerate delivery, and review AI-generated code with the same rigor as human-written code
- Build on shared platforms and reusable components rather than from scratch, and decide what stays specific to a client and what should be generalized for future engagements
- Define acceptance criteria, measure technical performance and business outcomes, and report results honestly, including when they fall short
- Lead technical workshops, architecture reviews, proofs of value, and solution demonstrations for client engineers, architects, business owners, and executives
- Communicate technical trade-offs, feasibility assessments, risks, and recommendations clearly to both technical and non-technical audiences
- Deliver technical documentation, runbooks, training, and handover so client teams can operate and extend the solutions independently
- Establish engineering best practices within the engagement, mentor other engineers, and drive AI-native adoption
- Share client context across the team so no single person becomes a point of failure, and keep current with a rapidly changing AI toolset
- Senior-level software engineering experience, with a proven track record of building and operating production-grade software, including AI solutions, in enterprise environments
- Hands-on experience with agentic AI, LLM application development, prompt and context engineering, tool calling, orchestration, retrieval-augmented generation (RAG), and AI evaluation
- Daily hands-on use of AI coding agents such as Claude Code, Codex, or GitHub Copilot, with the judgment to review and correct the code they generate
- Strong backend development skills in at least one of Python, Java, or Node.js, including API design, microservices, and SQL and NoSQL data stores, plus sufficient modern frontend skills (for example React and TypeScript) to deliver usable end-to-end solutions
- Practical experience with at least one major cloud (AWS, Azure, or Google Cloud), containers and Kubernetes, infrastructure as code (Terraform or Bicep), CI/CD, and observability
- Experience designing or contributing to end-to-end solution architecture, including system integration, data flows, security, scalability, resilience, and cost, with decisions documented and defended with client architects
- Proficiency in at least one major AI platform or ecosystem, such as OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, or Microsoft Foundry, with the ability to work across different technology stacks
- Experience integrating AI applications with enterprise systems, databases, APIs, and systems of record such as SAP, Salesforce, ServiceNow, or Microsoft Dynamics
- Understanding of production AI requirements, including identity and access management, data governance, observability, guardrails, human oversight, failure handling, and running cost
- Ability to take a loosely defined problem to a production implementation with minimal direction, and to decide what to build and why, not only how
- Experience working directly with clients, conducting technical discovery and process mapping with the people who run the work, facilitating workshops, presenting architecture decisions, and managing stakeholder expectations up to executive level
- Strong analytical, consulting, and communication skills, with the ability to explain technical concepts and trade-offs in clear business terms, including when a requirement is infeasible or the wrong approach
- Experience preparing technical documentation and delivering training or handover to client engineering and IT teams
- Practical knowledge of at least one business domain, such as financial services, healthcare, insurance, retail, manufacturing, telecommunications, logistics, or the public sector, including relevant regulatory and compliance requirements
- English proficiency at B2 level or higher
- Current partner certifications from Anthropic, Google, or OpenAI are a plus. EPAM supports engineers in obtaining and maintaining them