Lead Engineer - Agentic AI

Job highlights

• You will own these systems across the entire agentic development lifecycle : use-case scoping,multi-agent architecture and tool design,orchestration,evaluation,guardrails,deployment,observability,and continuous improvement. Technical Skills and Experience . Bachelors degree in Computer Science,Engineering,or equivalent practical experience
• Required Skills

”> Lead Engineer - Agentic AI About QualMinds

At QualMinds, we design and develop world-class digital products and custom software solutions for Startups, Scale-ups, and Enterprises. Our engineering teams specialize in Frontend, Backend, Full Stack Development, QA Automation, UI/UX, DevOps, Data Science, Machine Learning, and Agile Delivery. We are passionate about building customer-centric software with the highest standards of quality, scalability, security, performance, and reliability.

Location: Hyderabad, India

Experience: 8+ years (2+ years in production LLM/agentic systems, with proven multi-agent architecture experience)

Job Summary

We are building multi-agent AI systems for the automotive dealership ecosystem - networks of specialized, collaborating agents spanning customer-facing conversational experiences (voice and chat agents on dealership websites that handle inventory search, payment estimation, trade-in valuation, offers, test drive and service scheduling, and lead capture) and internal business agents (conversational and autonomous operation of campaign management, lead management, offers, payments, reporting, forecasting, and email/SMS marketing - with proactive insights, recommendations, and human-approved actions).

These are not single-chatbot solutions: you will architect orchestrated teams of agents - planners, retrievers, domain specialists, action executors, and reviewer/critic agents - that coordinate, hand off, and share context to complete complex business workflows reliably. You will own these systems across the entire agentic development lifecycle : use-case scoping, multi-agent architecture and tool design, orchestration, evaluation, guardrails, deployment, observability, and continuous improvement.

Technical Skills and Experience

• Bachelors degree in Computer Science, Engineering, or equivalent practical experience.
• Proven experience designing and shipping multi-agent architectures : supervisor/orchestrator patterns, hierarchical and peer-to-peer agent topologies, planner-executor-critic loops, agent handoffs, task decomposition and delegation, shared state/memory design, and failure isolation between agents.
• Hands-on with current agent frameworks and SDKs with strong multi-agent support: LangGraph, OpenAI Agents SDK, Google ADK, Anthropic Claude Agent SDK, Microsoft Agent Framework, Pydantic AI, or Vercel AI SDK - including subagents, handoffs, and graph-based orchestration.
• Deep experience with frontier model APIs: OpenAI (GPT-5.x, Realtime API), Anthropic (Claude Opus/Sonnet 4.x), Google (Gemini 3.x, Gemini Live) ; familiarity with open-weight models (Llama, Qwen, DeepSeek, Mistral) for per-agent cost/latency routing (eg, small fast models for routing/critic agents, frontier models for planning).
• Expert in tool/function calling, structured outputs, MCP (Model Context Protocol) - designing and building MCP servers that expose business systems (inventory, CRM, campaigns, payments, reporting) as governed agent tools; working knowledge of A2A (Agent2Agent) for cross-platform agent interoperability.
• Realtime voice AI : speech-to-speech pipelines (OpenAI Realtime API, Gemini Live, LiveKit Agents, Pipecat), STT/TTS providers (Deepgram, ElevenLabs, Cartesia), barge-in/interruption handling, sub-second latency optimization, WebRTC and telephony integration - including voice agents that delegate to backend specialist agents mid-conversation.
• Production RAG and context engineering : vector stores (pgvector, Qdrant, Vertex AI Search, Pinecone), hybrid retrieval and reranking, context window management across agent hops, shared and per-agent memory (short-term, long-term, episodic), and freshness strategies for fast-changing data (inventory, offers, pricing).
• Agent evaluation and observability : LangSmith, Braintrust, Arize Phoenix, Langfuse, or custom harnesses - golden datasets, LLM-as-judge, multi-agent trajectory evals (correct delegation, handoff quality, loop/runaway detection)…