Forward Deployed Engineer (Chief Role)

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 builders 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.

• 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

• 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

• 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