Role overview We are hiring a senior AI engineer to build and lead agentic AI systems: LLM agents that plan, call tools and complete real business tasks end to end. You will build agents that hold live phone and web conversations — streaming speech recognition, reasoning and tool calls, and speech synthesis in real time. You will stay hands-on while setting technical direction, owning the evaluation system that proves each agent works before every release, and helping a team of AI engineers move use cases from a demo to a reliable, observable service that clients trust with their customers. Key responsibilities Agentic systems • Design and build LLM agents that plan, call tools and complete multi-step business tasks across voice, chat and back-office channels. • Implement task and conversation state, memory and context management, retrieval (RAG) and model routing between fast and more capable models. • Define tool schemas and permissions, validate every tool call, and add human approval points for high-impact actions. • Build durable workflows with checkpoints, retries, idempotency keys, timeouts and compensation for failed actions. • Use multi-agent designs, such as a router with specialist agents, only where they measurably beat a single agent. • Integrate CRMs, ticketing systems and databases for actions such as complaint registration, classification, routing, status updates and notifications, with warm human handoff. Voice agents • Build streaming pipelines that connect telephony (SIP, Twilio, Exotel) and WebRTC audio to ASR, LLMs and TTS over WebSockets. • Implement voice activity detection, turn detection, barge-in and cancellation so the agent stops talking the moment a caller speaks. • Set and hold latency budgets per hop (ASR, LLM, TTS, network), with a target of under [1 s] from end of caller speech to first agent audio. • Adapt agent behaviour for speech: short turns, confirmations before actions, and recovery from mishearing. • Support multilingual and code-switched calls, including language detection and matching TTS voices. Evaluation • Build and maintain golden test sets that define the user goal, persona, expected tool calls and pass criteria for each scenario. • Run trajectory evals that check each step, tool choice, arguments and order, not just the final answer. • Run simulated-caller evals for voice: an LLM plays the caller, TTS voices it with accents, noise and interruptions, and the agent is tested end to end over real audio. • Score task success, tool-call correctness, policy compliance, hallucination, word error rate and latency, using rule checks and LLM judges calibrated against human ratings. • Gate releases in CI on eval results, block regressions, and turn every confirmed production failure into a new test case. Backend and operations • Write async Python services (FastAPI, asyncio) with structured logging, tracing and metrics for every agent run and conversation. • Load-test concurrent sessions, find bottlenecks and keep p95 latency and per-task model cost within budget. • Protect sensitive data: authentication and authorisation, audit trails for agent actions, PII redaction and tenant isolation. • Ship with Docker and CI using staged releases and rollback, and troubleshoot live agents in production. • Work with product and delivery teams to turn client workflows into working agent integrations. Technical leadership • Own the architecture of agent and voice systems, document key decisions and explain trade-offs to engineers, product and clients. • Break client requirements into technical designs and delivery milestones, and assign work across the team. • Review code and designs, and set team standards for testing, evaluation, monitoring and release. • Mentor AI engineers through pairing, design reviews and feedback, and help them grow into owning features end to end. • Lead incident response for production issues, run blameless post-mortems and drive the fixes to completion. Required qualifications and skills • Strong Python and backend skills: APIs, asyncio, WebSockets, structured error handling and automated tests. • Hands-on experience shipping LLM agents beyond a prompt demo: tool calling, state management, RAG and failure handling in a real workflow. • Sound judgement on agent design: when a fixed workflow beats an autonomous agent, single versus multi-agent, and how to keep agents safe with permissions and guardrails. • Working knowledge of a real-time voice stack: streaming ASR and TTS, VAD, turn-taking and the trade-offs between latency and accuracy. • Experience designing evaluations for LLM agents: test sets, trajectory and outcome metrics, LLM-as-judge and regression tracking — and knowing when a metric is misleading. • Ability to debug across services using logs, traces and recorded sessions to find where an agent failed or slowed down. • Experience with SQL or NoSQL databases, Git, Docker and CI pipelines. • Evidence of owning production services from design through operation, including reliability under real traffic. • Experience mentoring engineers, reviewing their work and leading technical decisions with product or client stakeholders. • Bachelor’s degree in a technical field or equivalent practical experience, with clear examples of delivered work. Preferred experience • Agent frameworks and orchestration tools such as LangGraph, OpenAI Agents SDK, Temporal or MCP tool servers. • Production voice agents on telephony (SIP, Twilio, Exotel) or WebRTC, with frameworks such as LiveKit Agents or Pipecat. • Speech providers such as Deepgram, Whisper, Google STT, ElevenLabs, Azure TTS or Cartesia, and measuring their WER and latency on your own data. • Indian-language and code-switched speech (Hindi, Telugu, Tamil and English). • Queues, caching and event-driven systems (Redis, Kafka, RabbitMQ), and load-testing tools such as Locust or k6. • Access controls, PII redaction and on-premises or restricted-network deployments. • Other modalities, such as vision or document understanding (images, screenshots, scanned forms), and combining them with voice or text in one agent. Success in the role Success means AI agents — voice, chat or back-office — that complete real business tasks reliably, with evidence that they work, and a team that ships them with confidence. In your first six months, that looks like: • Business outcomes: agents measurably resolve tasks without human help, cut handling time and raise customer satisfaction for client use cases. • Reliability: workflows recover from timeouts, tool failures and bad input; write actions are never duplicated; every incident is traced to a cause and fixed. • Evaluation culture: every release is gated by evals, metrics track what users actually experience, and production failures flow back into the test sets. • Performance and cost: latency and per-task model cost stay within agreed budgets as traffic grows. • Delivery: client requirements become working integrations on schedule, with clear handover to product and delivery teams. • Engineering quality: features are tested, observable, documented and easy for others to maintain. • Team growth: architecture decisions are documented, engineers you mentor own features end to end, and the team ships faster with fewer production issues.