Chief Forward Deployed Engineer
⚲ Poznan
Do uzgodnienia
Wymagania
- Large Language Models
- Langchain
- vector databases
- Embeddings
- RAG
- Agent design
- Python
- Model Evaluation
Opis stanowiska
We are looking for a Chief Forward Deployed Engineer to build AI-native solutions 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 will 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 — interviews, UAT, 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 (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 loop
• The ability 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 (LangChain, LangGraph, Semantic Kernel, or similar) and major LLM providers (OpenAI, Anthropic, Google Gemini)
• Expert-level 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, custom eval frameworks — and using observability/tracing tools (LangSmith, Arize Phoenix, Langfuse, or similar)
• Production deployment experience on at least one major cloud (AWS, Azure, or GCP) with containerization, 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, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms (AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry, Gemini Enterprise)
• Exposure to AI governance, security, and compliance (guardrails, prompt-injection prevention)
• Prior client-facing or pre-sales exposure in a consulting or services context
We offer
• We gather like-minded people:Engineering community of industry professionals
• Friendly team and enjoyable working environment
• Flexible schedule and opportunity to work remotely within Poland
• Chance to work abroad for up to 60 days annually
• Business-driven relocation opportunities
• We provide growth opportunities:Outstanding career roadmap
• Leadership development, career advising, soft skills, and well-being programs
• Certification (GCP, Azure, AWS)
• Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru
• English classes
• We cover it all:Stable income (Employment Contract or B2B)
• Participation in the Employee Stock Purchase Plan
• Benefits package (health insurance, multisport, shopping vouchers)
• Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more
• Referral bonuses
• Corporate, social and well-being events
• Please, note:The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview.
• We will reach out to selected candidates exclusively.
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
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 — interviews, UAT, 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 (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 loop
• The ability 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 (LangChain, LangGraph, Semantic Kernel, or similar) and major LLM providers (OpenAI, Anthropic, Google Gemini)
• Expert-level 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, custom eval frameworks — and using observability/tracing tools (LangSmith, Arize Phoenix, Langfuse, or similar)
• Production deployment experience on at least one major cloud (AWS, Azure, or GCP) with containerization, 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, Agent Skills, and emerging agent standards
• Experience with enterprise AI platforms (AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry, Gemini Enterprise)
• Exposure to AI governance, security, and compliance (guardrails, prompt-injection prevention)
• Prior client-facing or pre-sales exposure in a consulting or services context
We offer
• We gather like-minded people:Engineering community of industry professionals
• Friendly team and enjoyable working environment
• Flexible schedule and opportunity to work remotely within Poland
• Chance to work abroad for up to 60 days annually
• Business-driven relocation opportunities
• We provide growth opportunities:Outstanding career roadmap
• Leadership development, career advising, soft skills, and well-being programs
• Certification (GCP, Azure, AWS)
• Unlimited access to LinkedIn Learning, Get Abstract, Cloud Guru
• English classes
• We cover it all:Stable income (Employment Contract or B2B)
• Participation in the Employee Stock Purchase Plan
• Benefits package (health insurance, multisport, shopping vouchers)
• Strategically located offices featuring entertainment and relaxation zones, table tennis and football, free snacks, fantastic coffee, and more
• Referral bonuses
• Corporate, social and well-being events
• Please, note:The set of bonuses might vary based on the role you apply for – specifics will be discussed with our recruiter during the general interview.
• We will reach out to selected candidates exclusively.
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our customers, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
🔍 Dekoder Ogłoszenia
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Chief Forward Deployed Engineer
To stanowisko może oznaczać, że będziesz pracować w pierwszej linii, blisko klienta lub użytkownika końcowego, a nie tylko w wewnętrznym zespole deweloperskim.
🟡
AI-native solutions where LLM and its harness are the core of the value
Oznacza to, że będziesz budować rozwiązania, w których modele językowe i ich otoczka (narzędzia, integracje) są kluczowym elementem dostarczającym wartość, a nie tylko dodatkiem.
🟡
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.
Podkreśla, że będziesz aktywnie tworzyć kod produkcyjny i infrastrukturę, a nie tylko nadzorować proces.
🟡
Design for failure in the agent loop: retries, model fallbacks, cost limits, and human-in-the-loop on consequential actions
Wymaga to przemyślenia i implementacji mechanizmów radzenia sobie z błędami i nieprzewidzianymi sytuacjami w działaniu agentów AI.
🟡
Capture domain expertise and repeatable workflows — so what works on one engagement carries to the next
Oczekuje się, że będziesz dokumentować i generalizować rozwiązania, aby można je było wykorzystać w przyszłych projektach lub wdrożeniach.