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: You're 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) Solid debugging, logging, and QA instincts for hallucination, grounding, and UX behavior

📌 Ai Engineer – Voice Ai / Autonomous Agents (Bengaluru)
🏢 AtkinsRéalis
📍 Bengaluru