Senior AI Engineer - Agentic AI & Voice Agents

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.
• • Bachelors 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.