Location: Bengaluru, KA, India / Bengaluru, Karnataka, India

Job Type: Full-time

Salary: ₹3M - ₹6M INR

Experience: 6+ years

Skills: Python, LLMs, RAG, Fine-tuning, AI Agents

Role title: AI Engineer III

Location: Bengaluru (On-site)

Experience: 5+ years

About Vahan
Vahan is a marketplace for blue-collar work. Zomato, Zepto, Swiggy and others hire through us; hundreds of recruitment agencies source through us. Samvaadini, our hybrid voice and WhatsApp agent, places 100,000+ outbound calls a day to people looking for delivery and warehouse work across 900+ Indian cities — qualifying them, answering questions about pay and shift timings, and handing the serious ones to a recruiter. It speaks Hindi, Hinglish, and a widening set of regional languages, mostly to people on low-end Android phones in noisy streets, many of whom have never used a chatbot before. When Samvaadini gets better, more people get placed in a job this week instead of next month.

The Role
You'll own outcomes across the system, not one component — the voice and WhatsApp agents, the eval suite, the conversion funnel they feed, and root-cause analysis across code, agent behaviour, models, and data. At this level you'll more often be handed a number that moved than a task to complete: qualified handoffs down 12%? You localize it across code, agent behaviour, a silent model change, upstream data, or a market shift, then argue for the fix with the most leverage.

What You'll Do

  • Own the outcome, not the ticket — investigate metric moves end to end and argue for the highest-leverage fix (sometimes that's doing nothing)

  • Know the funnel cold: outreach, connect, qualify, handoff, interview, placement — write your own queries

  • Own agent behaviour end to end: the loop, tool surface, context management, stop conditions, retries, and timeout handling

  • Build and defend the eval suite — every behaviour change ships behind a golden set and a regression gate

  • Fight for latency and cost — time-to-first-audio and inference cost at 100K calls/day are yours to defend

  • Work the voice pipeline — streaming ASR on code-switched Hinglish, turn detection, barge-in, graceful failure on network drops

  • Debug production non-determinism — reproduce rare failures, write postmortems, close the loop with evals

  • Use coding agents hard and well — Claude Code from Day 1

What We're Looking For

  • Shipped an LLM-backed system in production, with real users, long enough for it to break

  • Can trace a business metric to its root cause across any layer — as comfortable in a warehouse query as a stack trace

  • Strong async Python — you write services, not notebooks

  • Brings up evaluation before we do

  • Model-choice opinions grounded in latency and cost — and you change them when shown better numbers

  • Fluent with coding agents; can name the last time one confidently produced something wrong and how you caught it

  • Can explain a technical trade-off to a recruiter or ops lead without dumbing it down

  • Learns fast in public

Nice to Have (Genuinely Not Required)
Real-time voice stacks (LiveKit, Pipecat, Deepgram, ElevenLabs, Cartesia, telephony); Hindi/Hinglish or code-switched NLP; fine-tuning/distillation; durable workflow orchestration; ClickHouse or comparable analytical SQL; open-source work or public writing; MCP server authoring.

Summary

Own outcomes across a high-volume voice and WhatsApp agent system, from eval suite and conversion funnel to root-cause analysis across code, agent behavior, models, and data; requires a shipped production LLM system, strong async Python, metric-to-root-cause tracing, evaluation focus, and coding-agent fluency.

Responsibilities

Own outcomes across voice/WhatsApp agents, eval suite, conversion funnel, and root-cause analysis; know the funnel; own agent behavior; build evals; defend latency/cost; work voice pipeline; debug production non-determinism; use coding agents.

Qualifications

Shipped an LLM-backed system in production; strong async Python; trace business metrics to root cause across layers; bring up evaluation; fluent with coding agents; explain technical trade-offs; learn fast.

Experience requirements

5+ years; shipped production LLM system; strong async Python; metric-to-root-cause tracing; evaluation focus; coding-agent fluency.