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.

• 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.