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AI Engineer - Voice AI / Autonomous Agents

5 - 10 years

14 - 17 Lacs

Posted:1 day ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

AI Engineer - Voice AI / Autonomous Agents

Who are we?

We are Spyne, redefining how cars are marketed and sold with cutting-edge

Generative AI

. What started as a bold idea using AI-powered visuals to help dealers sell online faster has evolved into a full-fledged

AI-first automotive retail ecosystem

.
Backed by

$16M in Series A funding

from

Vertex Ventures, Accel

, and other top investors, we re scaling fast:
Expanded across the

US & EU markets

Launched

industry-first AI-powered Image & 360 solutions

Achieved a

5X revenue surge in 15 months

, aiming for

3-4X growth this year

Now, we re rolling out an

end-to-end GenAI Automotive Retail Suite

and pushing into the

US market

to bring our AI-driven products to

3,000-4,000 dealers

.

Read more about us:

Studio AI Product

- t.ly/t0Ko5

Retail AI Product -

t.ly/EyKC9

Series A Announcement -

Spyne raises $16 Mil!
Spyne raising another round!!
Spyne secures funding for US Expansion!

What are we looking for?

We are looking for a

Senior AI Engineer - Voice AI / Autonomous Agents

to own and build Spyne s in-house voice bot stack. This is a high-impact individual contributor role at the intersection of

LLMs, ASR/TTS, and voice UX

, focused on creating deeply human, latency-optimized conversations between auto dealerships and their customers.
Location: Gurugram (Work from Office, 5 days a week)

What will you do?

Voice AI Stack Ownership:

Build and own the end-to-end voice bot pipeline ASR, NLU, dialog state management, tool calling, and TTS to create a natural, human-like conversation experience.

LLM Orchestration & Tooling:

Architect systems using

MCP (Model Context Protocol)

to mediate structured context between real-time ASR, memory, APIs, and the LLM.

RAG Integration:

Implement

retrieval-augmented generation

to ground responses using dealership knowledge bases, inventory data, recall lookups, and FAQs.

Vector Store & Memory:

Design scalable

vector-based search

for dynamic FAQ handling, call recall, and user-specific memory embedding.

Latency Optimization:

Engineer low-latency, streaming ASR + TTS pipelines and fine-tune turn-taking models for natural conversation.

Model Tuning & Hallucination Control:

Use

fine-tuning, LoRA

, or instruction tuning to customize tone, reduce hallucinations, and align responses to business goals.

Instrumentation & QA Looping:

Build robust observability, run real-time call QA pipelines, and analyze interruptions, hallucinations, and fallbacks.

Cross-functional Collaboration:

Work closely with product, infra, and leadership to scale this bot to thousands of US dealerships.

What will make you successful in this role?

Architect-level thinking:

You understand how ASR, LLMs, memory, and tools fit together and can design modular, observable, and resilient systems.

LLM Tooling Mastery:

You ve implemented

tool calling, retrieval pipelines, function calls

, or prompt chaining across multiple workflows.

Fluency in Vector Search & RAG:

You know how to chunk, embed, index, and retrieve and how to avoid prompt bloat and token overflow.

Latency-First Mindset:

You debug token delays, know the cost of each API hop, and can optimize round-trip time to keep calls human-like.

Grounding > Hallucination:

You know how to trace hallucinations back to weak prompts, missing guardrails, or lack of tool access and fix them.

Prototyper at heart:

Youre not scared of building from scratch and iterating fast, using open-source or hosted tools as needed.

What you must have

  • 5+ years in AI/ML or voice/NLP systems with real-time experience
  • Deep knowledge of

    LLM orchestration

    ,

    RAG

    ,

    vector search

    , and

    prompt engineering

  • Experience with

    MCP-style architectures

    or structured context pipelines between LLMs and APIs/tools
  • Experience integrating ASR (Whisper/Deepgram), TTS (ElevenLabs/Coqui), and OpenAI/GPT-style models
  • Solid understanding of latency optimization, streaming inference, and real-time audio pipelines
  • Hands-on with

    Python

    ,

    FastAPI

    , vector DBs (Pinecone, Weaviate, FAISS), and cloud infra (AWS/GCP)
  • Strong debugging, logging, and QA instincts for hallucination, grounding, and UX behavior

Why Spyne?

Real-world AI impact at scale
A-Grade team that balances speed with technical depth
High autonomy and visibility from Day 1
Rapid growth = faster career acceleration
MacBook + all the tools and compute you need
Flat structure, no BS, just real work

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