Back to SearchForward Deployed AI Engineer - Agentic Systems Lead
Hybrid in United States of America
AI Solution Engineering& 2 others
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Step into a high-impact role where you'll own end-to-end delivery of AI-native solutions embedded directly within client environments. As a Forward Deployed Expert, you'll design and operate agentic frameworks that solve real business problems, not just build prototypes.
Join EPAM to engineer solutions that matter. From AI to cloud transformation, you'll collaborate with top-tier innovators, gain autonomy to explore your ideas, and grow your skills in a culture built for tech excellence.
Req.#1100243513
Responsibilities
- Own technical outcomes end-to-end as the AI-Native Delivery Manager's direct counterpart, driving workstream completion beyond task execution
- Design, build, and operate agentic frameworks, tools, evaluations, and infrastructure that power production AI-native solutions
- Work embedded with client business and technical stakeholders, adapting agentic systems to real integration, data, and security constraints
- Partner with delivery managers on scope and SLA definition, providing technical grounding for realistic commitments and timelines
- Contribute engagement learnings to the delivery playbook, documenting staffing patterns, tooling choices, and integration insights for future teams
- Mentor less experienced engineers on the FDE pillar spectrum, helping them grow from build-focused roles to full delivery ownership
Requirements
- 8+ years of software engineering experience with recent hands-on leadership of agentic or LLM-based systems in production environments
- Deep expertise in agent architecture and orchestration, including goal decomposition, multi-agent coordination, and autonomy-versus-handoff decision frameworks
- Proven experience grounding models in live data systems through retrieval, context construction, and maintaining accuracy against changing knowledge sources
- Ability to design guardrails, evaluations, and observability for agentic failure modes including hallucination, tool-call errors, and runaway loops
- Understanding of production trade-offs such as latency, cost per call, rate limits, versioning, and rollback under fixed-fee or managed-output SLAs
- Demonstrated ownership of technical workstream outcomes with accountability for results, including integration into client environments with security and legacy constraints