We're looking for a Technology Consultant/Forward Deployed Engineer (FDE) who can work directly with enterprise clients, identify their most impactful problems, and solve them using production-ready AI solutions. In this role, you'll own problems completely—from technical discovery and feasibility through architecture, build, deployment, and measurable business results. You'll be embedded on the client side, gain deep knowledge of their business, and redesign their processes with AI built on the platforms, data, and systems they already use. This role combines hands-on AI and full-stack engineering, solution architecture, and technical consulting. You'll work with 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, you'll specialize in one of three areas: Partner Platform Expert, focused on a single AI partner's platform such as Anthropic, OpenAI, Google Gemini and Vertex AI, AWS Bedrock, or Microsoft Foundry; SDLC and AI Software Factory Expert, helping client engineering teams move measurably faster using harnesses and AI software factories; or AI Technical Solutions Expert, turning client use cases into production AI systems built on the client's existing platforms. Your success will be measured by outcomes the client can see and sustain—not by features shipped or hours billed. Responsibilities 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 actual business processes, including exceptions, and determine which steps to automate, which should keep a human in the loop, and which should remain fully manual Assess feasibility and recommend the best-fit approach, whether that's 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 Build, test, and deploy production-grade, full-stack AI applications yourself 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, reviewing AI-generated code with the same rigor applied to human-written code Build on shared platforms and reusable components instead of starting from scratch, deciding what remains client-specific and what should be generalized for future use Define acceptance criteria, measure technical performance and business outcomes, and report results honestly—including shortfalls Lead technical workshops, architecture reviews, proofs of value, and solution demos 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 Create technical documentation, runbooks, training materials, and handover packages so client teams can operate and extend solutions independently Establish engineering best practices within the engagement, mentor other engineers, and drive adoption of AI-native approaches Share client context across the team to avoid single points of failure, and keep up with a fast-changing AI toolset Requirements Senior-level software engineering experience with a track record of building and operating production-grade software, including AI solutions, in enterprise settings 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 generated code Strong backend skills in at least one of Python, Java, or Node.js, including API design, microservices, and SQL/NoSQL data stores, plus enough modern frontend skill (e.g., React and TypeScript) to deliver complete end-to-end solutions Practical expertise in at least one major cloud platform (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 the ability to document and defend decisions alongside client architects Proficiency with at least one major AI platform or ecosystem (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, or Microsoft Foundry), with the flexibility to work across different tech stacks Experience integrating AI applications with enterprise systems, databases, APIs, and systems of record such as SAP, Salesforce, ServiceNow, or Microsoft Dynamics Solid understanding of production AI requirements, including identity and access management, data governance, observability, guardrails, human oversight, failure handling, and cost management Ability to take a loosely defined problem to a production implementation with minimal guidance, deciding not just how to build something but what to build and why Experience working directly with clients—conducting technical discovery and process mapping with the people doing the work, running workshops, presenting architecture decisions, and managing expectations up to the executive level Strong analytical, consulting, and communication skills, able to translate technical concepts and trade-offs into clear business terms, including explaining when a requirement isn't feasible or is the wrong approach Experience creating 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 Nice to have Active partner certifications from Anthropic, Google, or OpenAI are a plus, and EPAM supports engineers in earning and maintaining them
