Location-Bengaluru, India

Employment Type-Full time

Level-Consultant (5+ Years}

Role Summary

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.

Key Responsibilities

What you'll do

•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.

Must Have

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



solutions architect, consulting engineer, delivery tech lead).

•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.

Preferred / Nice to Have

•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.

📌 Senior Forward Deployed Engineer (Bengaluru)
🏢 08World
📍 Bengaluru