We're seeking a Forward Deployed Engineer to work directly alongside enterprise clients, identifying their most valuable challenges and delivering production-ready AI solutions to address them. You'll take complete ownership of each problem, handling technical discovery, feasibility assessment, architecture design, development, deployment, and delivering measurable business results. Working on-site with clients, you'll gain deep knowledge of their business operations and redesign their workflows around AI, leveraging the platforms, data, and systems already in place. This position blends hands-on AI and full-stack development, solution architecture, and technical advisory work. You'll engage with AI models, agent frameworks, data and retrieval systems, cloud infrastructure, and enterprise integrations, leveraging AI coding agents and agentic engineering methods to deliver results quickly. Based on the specific engagement, you'll focus on one of three specializations: Partner Platform Expert, specializing in 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, accelerating client engineering teams through harnesses and AI software factories; or AI Technical Solutions Expert, converting client use cases into production AI systems built on the client's current platforms. Your success will be judged by outcomes that clients can observe and maintain, rather than by features delivered or hours logged. Responsibilities Work directly within client business and technical teams to pinpoint high-value problems and take full ownership of them, covering discovery, specification, architecture, build, production, and monitoring Document actual business processes, accounting for exceptions, and determine which steps to automate, which require human oversight, and which should remain manual Evaluate feasibility and suggest the best-fit approach, be it agentic AI, deterministic workflows, traditional machine learning, or forgoing AI altogether Create comprehensive solution architectures addressing models, orchestration, data and retrieval, enterprise integrations, identity, security, observability, and cost Build, test, and deploy production-grade, full-stack AI applications directly on clients' existing cloud and AI platforms Develop agent workflows, retrieval systems, tool integrations, evaluation frameworks, guardrails, monitoring, and human-in-the-loop controls Leverage AI coding agents and agentic engineering methods to speed up delivery, applying the same scrutiny to AI-generated code as to human-written code Develop using shared platforms and reusable components instead of starting from scratch, determining what remains client-specific versus what should be generalized for future use Establish acceptance criteria, track technical performance and business outcomes, and report results transparently, including shortfalls Facilitate technical workshops, architecture reviews, proofs of value, and solution demonstrations for client engineers, architects, business owners, and executives Convey technical trade-offs, feasibility findings, risks, and recommendations clearly to technical and non-technical stakeholders alike Produce technical documentation, runbooks, training materials, and handover packages enabling client teams to independently operate and extend solutions Set engineering best practices within the engagement, mentor fellow engineers, and promote AI-native adoption Distribute client knowledge across the team to avoid single points of failure, staying current with the fast-evolving AI toolset Requirements Senior-level software engineering background, with demonstrated success building and operating production-grade software, including AI solutions, within 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 Regular hands-on use of AI coding agents such as Claude Code, Codex, or GitHub Copilot, with sound judgment to review and fix the code they produce Solid backend development skills in at least one of Python, Java, or Node.js, covering API design, microservices, and SQL and NoSQL data stores, along with sufficient modern frontend skills (such as React and TypeScript) to deliver complete end-to-end solutions Hands-on experience with at least one major cloud provider (AWS, Azure, or Google Cloud), containers and Kubernetes, infrastructure as code (Terraform or Bicep), CI/CD, and observability Background designing or contributing to end-to-end solution architecture, covering system integration, data flows, security, scalability, resilience, and cost, with decisions documented and justified to client architects Skill in at least one major AI platform or ecosystem, such as OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, or Microsoft Foundry, along with the flexibility to work across different technology stacks Background integrating AI applications with enterprise systems, databases, APIs, and systems of record such as SAP, Salesforce, ServiceNow, or Microsoft Dynamics Knowledge of production AI requirements, covering identity and access management, data governance, observability, guardrails, human oversight, failure handling, and running cost Capability to move a loosely defined problem to a production implementation with minimal guidance, deciding what to build and why, not just how Background working directly with clients, performing technical discovery and process mapping alongside the people who run the work, leading workshops, presenting architecture decisions, and managing stakeholder expectations through to executive level Solid analytical, consulting, and communication abilities, with the capacity to explain technical concepts and trade-offs in clear business terms, including flagging when a requirement is infeasible or misguided Background preparing technical documentation and delivering training or handover to client engineering and IT teams Hands-on knowledge of at least one business domain, such as financial services, healthcare, insurance, retail, manufacturing, telecommunications, logistics, or the public sector, including applicable regulatory and compliance requirements English proficiency at B2 level or higher Nice to have Current partner certifications from Anthropic, Google, or OpenAI (EPAM supports engineers in obtaining and maintaining them)
