Sr. Forward Deployed Engineer

You own client outcomes. You lead AI deployments end-to-end — from technical discovery to production adoption — for our most strategic accounts. You write code, architect solutions, manage the client relationship at a technical level, and mentor Associate FDEs. You're the person the client calls when something breaks and the person the product team listens to when something needs to change.

• Lead technical discovery: understand the client's data, systems, constraints (SOC 2, DPDP, data residency), and success metrics.

• Architect and ship LLM and agentic solutions to production — retrieval, orchestration, tool integrations, evals, observability, cost/latency tuning.

• Write production-grade code; know when to build vs. push back to product.

• Own live incidents on your accounts: diagnose, fix, communicate.

• Run steering conversations with client engineering and business stakeholders.

• Mentor Associate FDEs; review their code, prompts, and client comms.

• Turn recurring client pain into concrete roadmap input for the product team.

• 5+ years in engineering, with at least 2 in client-facing or deployment-heavy roles (FDE, solutions architect,

• Strong Python and SQL; production experience on at least one cloud.

• Shipped at least one LLM-powered system to production — not just POCs — and owned it through iteration.

• Deep, hands-on knowledge of agentic frameworks (LangGraph, LlamaIndex, CrewAI, Autogen, Semantic Kernel, or equivalent) with a clear point of view on when to use what Strong grasp of RAG architectures — hybrid search, re-ranking, chunking strategies, retrieval evals.

• Experience with LLM evaluation and observability — building eval sets, tracking regressions, tools like

LangSmith, Langfuse, Braintrust, or equivalent.

• Judgment on guardrails, prompt injection, PII handling, model selection, and cost/latency trade-offs.

• Ability to hold a technical conversation with a client CTO and a debugging session with a junior engineer in the same afternoon.

• Fine-tuning, distillation, or model routing in production.

• Data platform depth — Fabric/OneLake, Databricks, Snowflake.

• Privacy/compliance work (DPDP, GDPR, ISO 27001).

• Pre-sales — scoping, estimating, writing SOWs