Lead AI Engineer

Role title: AI Engineer III Location: Bengaluru (On-site) Experience: 5+ years About VahanVahan 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 RoleYou'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.