AI Engineer - Voice AI & Agents (GCP ADK)

The Role

We are hiring AI Engineers to build the AI core of our platform: production voice AI agents and multi-agent workflows that handle real mortgage conversations and processes for enterprise lenders across North America. This role demands hands-on depth with Google Cloud's Agent Development Kit (ADK) and real-time voice agent development — not just API calls, but latency budgets, barge-in handling, tool orchestration, and evals.

Location: Onsite (Pune)

Working Hours: 7.30 pm to 4.30 am

What You'll Do

Design and build production agentic systems using GCP Agent Development Kit (ADK) and Vertex AI (Gemini)

Develop real-time Voice AI agents: streaming STT/TTS, turn-taking, interruption/barge-in handling, and telephony integration (Twilio/SIP)

Architect multi-agent orchestration: tool calling, function routing, memory, guardrails, and human handoff

Build RAG pipelines over mortgage documents and guidelines with strong grounding and citation behavior

Own quality: build eval harnesses, regression suites, and monitoring for non-deterministic agent behavior

Optimize for production: latency, cost, token budgets, caching, and fallback strategies across model providers

Work with Azure OpenAI alongside GCP — we are pragmatic and multi-cloud on models

Mentor engineers and set patterns for how the team builds with LLMs **

Must-Have Qualifications

5–8 years of software/ML engineering experience, with 2+ years building LLM-powered products in production

Hands-on experience with Google Cloud ADK (Agent Development Kit) and the Vertex AI / Gemini ecosystem

Proven experience building and shipping Voice AI agents — streaming speech-to-text/text-to-speech (Google Cloud Speech, Deepgram, ElevenLabs, or similar) and real-time conversational pipelines

Telephony/real-time audio experience: Twilio, LiveKit, SIP, WebRTC, or comparable

Strong Python engineering fundamentals — you write production code, not just notebooks

Deep practical understanding of agent design: tool calling, planning, state, guardrails, and failure handling

Experience designing evals and observability for LLM/agent systems

Nice to Have

Experience with Azure OpenAI, LangGraph, or other agent frameworks

Fine-tuning or model-optimization experience

Exposure to regulated domains (fintech, lending, healthcare) and compliance constraints on AI systems

Contributions to open-source AI tooling