Forward Deployed Engineer/Chief Role

EPAM Systems, Inc.; Turkey; On-site; TRY 4,423,000 - 7,371,000; Full time

Benefits offered by this job

Extra leave days

Referral bonuses

Private health insurance

Well-being events

IT equipment

Home Use Program (HUP)

English courses

Mentoring programs

Job summary

EPAM Systems, Inc. is seeking engineers to design, build and scale AI-native systems, including agents, workflows, and RAG harnesses. You will write production-grade Python, integrate with data sources, and deploy with modern CI/CD and cloud infrastructure.

You will collaborate with SMEs and end-users to validate real-world value, design for failure, and shape reusable, repeatable workflows across engagements.

Qualifications

• 7+ years of engineering experience building production AI/LLM applications.
• Strong agent-design judgment—task-harness fit and handling failures.
• Hands-on with agentic frameworks and major LLM providers.
• Expert-level Python and solid software engineering fundamentals.
• Experience with retrieval, vector databases, and observability tools.

Responsibilities

• Design, build and ship AI-native systems end-to-end: agents, workflows, RAG and harnessing.
• Write production-grade Python: integrations, APIs and data access.
• Collaborate with SMEs and end-users to validate real value and feasibility.
• Build evaluation pipelines to prove system usefulness and reliability.

Job description

We are building AI-native solutions for our clients — products where LLM and its harness are the core of the value.This is a builder's role: you and your team are responsible for building agentic systems, writing the 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 you design the feedback loops.ResponsibilitiesDesign, build and ship AI-native systems E2E — agents, workflows, RAG and the harness: custom tool calling, sandboxing, context engineering and sub-agents, caching, compactionBuild the evaluation pipelines and use them to prove the system is genuinely usefulDesign for failure in the agent loop: retries, model fallbacks, cost limits and human-in-the-loop on consequential actionsCapture domain expertise and repeatable workflows so what works on one engagement carries to the nextEngage early to help shape the use case and check technical feasibilityWrite production-grade Python: integrations, APIs, data access, deploymentWork directly with SMEs and end-users through interviews, UAT and observing the real workflow, and validate that the system fits how people actually workRequirements7+ years of engineering experience, with a strong recent track record building production AI / LLM applications (not 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 loopCapability 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 audiencesHands-on experience with agentic frameworks (LangChain, LangGraph, Semantic Kernel) and major LLM providers (OpenAI, Anthropic, Google Gemini)Expert-level Python and solid software engineering fundamentalsStrong RAG and retrieval skills: vector databases, embeddings, hybrid search, re-ranking, chunking and context managementProven experience evaluating generative AI quality — LLM-based evaluation, heuristics, custom eval frameworks — and using observability/tracing tools (LangSmith, Arize Phoenix, Langfuse)Production deployment experience on at least one major cloud (AWS, Azure, GCP) with containerization, CI/CDSound judgment under ambiguity — scoping, sequencing and making the call on speed vs. quality vs. scopeEnglish at C1 levelNice to haveExperience designing experiments, A/B testing and iterating on AI products against real user behavior and business metricsBackground in NLP, Data Science or applied ML, with experience moving models into productionFamiliarity with MCP, A2A and Agent Skills, and emerging agent standardsExperience with enterprise AI platforms (AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry)Exposure to AI governance, security and compliance (guardrails, prompt-injection prevention)We offerCONTINUOUS UPSKILLING, LEARNING & DEVELOPMENTDiversity of tasks and projectsAssessment center for objective review of competency levelPersonal development planMentoring programs and leadership developmentCertification and professional development supportAccess to learning platforms including more than 2,500 internal coursesEnglish courses taught by certified teachersCORPORATE BENEFITSExtra leave daysReferral bonusesCOMPENSATION PACKAGECompetitive compensation paid in USDRegular salary and performance reviewsMEDICAL & HEALTHCAREPrivate health insuranceWell-being eventsWORKING ENVIRONMENTRecreation areas and kitchensTea, coffee and snacksSports equipment and game consolesIT EquipmentMicrosoft’s Software Assurance Home Use Program (HUP)Please note that our Talent Attraction Team reviews applications and CVs submitted in English.EPAM is global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. With over thirty years of expertise in custom software, product and platform engineering, we empower our clients to become AI-Native enterprises, driving measurable value from innovation and digital investments.