Forward Deployed Engineer (Chief Role)
Primary stack
Nice to have's
Job description
About the Position
We are looking for a Forward Deployed Engineer (Chief Role) to build AI-native solutions where LLM and its harness are the core of the value. This is a builder's role where you and your team are responsible for building agentic systems, writing production code, and standing up the evals and observability. You will work closely with SMEs and end-users to understand where the real value lies and design the feedback loops.
Responsibilities
- Design, build and ship AI-native systems E2E - agents, workflows, RAG and the harness: custom tool calling, sandboxing, context engineering and sub-agents, caching, compaction
- Build the evaluation pipelines and use them to prove the system is genuinely useful
- Design for failure in the agent loop: retries, model fallbacks, cost limits and human-in-the-loop on consequential actions
- Capture domain expertise and repeatable workflows so what works on one engagement carries to the next
- Engage early to help shape the use case and check technical feasibility
- Write production-grade Python: integrations, APIs, data access, deployment
- Work directly with SMEs and end-users through interviews, UAT and observing the real workflow, and validate that the system fits how people actually work
Requirements
- 7+ years of engineering experience with a strong recent track record building production AI / LLM applications rather than prototypes or research only
- Strong agent-design judgment - task-harness fit, matching the harness to the context, failures and policies of the actual task rather than calling a model in a loop
- Capability to operate close to the client: lead discovery and feasibility conversations, work directly with SMEs and end-users, and explain technical trade-offs to both technical and non-technical audiences
- Hands-on experience with agentic frameworks such as LangChain, LangGraph or Semantic Kernel and major LLM providers including OpenAI, Anthropic and Google Gemini
- Expert-level proficiency in Python and solid software engineering fundamentals
- Strong RAG and retrieval skills: vector databases, embeddings, hybrid search, re-ranking, chunking and context management
- Proven experience evaluating generative AI quality - LLM-based evaluation, heuristics and custom eval frameworks - and using observability/tracing tools such as LangSmith, Arize Phoenix or Langfuse
- Production deployment experience on at least one major cloud such as AWS, Azure or GCP with containerization and CI/CD
- Sound judgment under ambiguity - scoping, sequencing and making the call on speed vs. quality vs. scope
- English at C1 level
Nice to Have
- Experience designing experiments, A/B testing and iterating on AI products against real user behavior and business metrics
- Background in NLP, Data Science or applied ML with experience moving models into production
- Familiarity with MCP, A2A and Agent Skills and emerging agent standards
- Experience with enterprise AI platforms such as AWS Bedrock AgentCore, Databricks Genie or Microsoft Foundry
- Exposure to AI governance, security and compliance including guardrails and prompt-injection prevention
We Offer
- Hybrid work environment in Bulgaria
- Opportunity to work on cutting-edge AI-native solutions
- Collaborative environment with SMEs and end-users
About the Company
[Company description if present]
© EPAM. This job description was sourced from the employer's public career page. TheJob is not the employer — we index the posting and route candidates to the source. All content rights and hiring decisions belong to the employer.
EPAM helps organizations innovate their business processes and rethink the way they manage their businesses so they can remain competitive in this new digital age.