Forward Deployed Engineer/Chief role (relocation to Lithuania)
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.
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 (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
- 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 (LangChain, LangGraph, Semantic Kernel) 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)
- Production deployment experience on at least one major cloud (AWS, Azure, 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 and Agent Skills, and emerging agent standards
- Experience with enterprise AI platforms (AWS Bedrock AgentCore, Databricks Genie, Microsoft Foundry)
- Exposure to AI governance, security and compliance (guardrails, prompt-injection prevention)
