Forward Deployed Engineer/Chief Role

EPAM Systems, Inc.; Turkey; On-site; TRY 5,772,000 - 8,658,000; Full time

Benefits offered by this job

Extra leave days

Referral bonuses

Private health insurance

Well-being events

Job summary

EPAM Systems, Inc. is seeking an engineering leader to design, build, and ship AI-native systems end-to-end. You will work closely with SMEs and end-users, developing agentic workflows, RAG integrations, and robust evaluation pipelines to ensure practical, production-ready outcomes.

You will lead the development of tool calling, sandboxing, and context engineering, with a strong emphasis on observability and reliable deployments across cloud environments.

Qualifications

• 7+ years of engineering experience with a strong track record building production AI/LLM applications.
• Expert-level Python and solid software engineering fundamentals.
• Hands-on experience with agentic frameworks and major LLM providers.
• Familiarity with observability/tracing tools and evaluation frameworks.

Responsibilities

• Design, build and ship AI-native systems end-to-end — agents, workflows, RAG and the harness.
• Build evaluation pipelines and prove system usefulness.
• Design for failure in the agent loop with retries, fallbacks and human-in-the-loop as needed.
• Capture domain expertise and create repeatable workflows across engagements.
• Write production-grade Python: integrations, APIs, data access, deployment.
• Engage with SMEs and end-users through interviews, UAT and observing real workflows.

Skills

Engineering experience

Agent design

Python

LangChain

LangGraph

Semantic Kernel

LLM providers

Vector databases

CI/CD

Cloud deployment

Tools

LangSmith

Arize Phoenix

Langfuse

AWS

Azure

GCP

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 a global leader in AI transformation engineering and integrated consulting, serving Forbes Global 2000 companies and ambitious startups. We deliver globally and engage locally, making the future real for clients, partners, and employees. We are proud to be recognised by Forbes, Glassdoor, Newsweek, Time Magazine, Great Place to Work and kununu as a Most Loved Workplace around the world.