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Senior AI Engineer - Agentic AI and Voice Agents

HyderabadOn-siteSenior

Good timing - competition is buildingPosted 4d ago

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The job post

1109 words · from YAL's careers site
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.

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