Lead Engineer – Agentic AI

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 Bachelor's 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 (e.g., 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), eval-gated CI/CD; OpenTelemetry GenAI semantic conventions with distributed tracing across agent boundaries. Guardrails and AI safety : prompt-injection defense, jailbreak resistance, PII redaction, content moderation, inter-agent trust boundaries, human-in-the-loop approval flows for consequential actions (payments, campaign sends, customer messaging), and compliance-aware messaging (TCPA). Strong Python and TypeScript ; modern backend (FastAPI, Node.js) and frontend ( React/Next.js ) development, including streaming and realtime UX (SSE, WebSockets, WebRTC) and generative UI patterns. Cloud-native delivery: GCP (Vertex AI, GKE, Pub/Sub, Cloud Run, IAM, Secret Manager) or equivalent on AWS/Azure; Docker, Kubernetes, Terraform; durable execution for long-running agent workflows (e.g., Temporal) is a plus. Modern engineering workflow: GitHub (Actions, PR reviews), Jira/Linear, prompt and agent versioning, feature-flagged and eval-gated releases. Effective daily use of AI-assisted development tools (GitHub Copilot, coding agents, Claude Code, Cursor) and the judgment to review their output critically. PostgreSQL and modern data tooling; event-driven architectures (Pub/Sub, Kafka) for agent-triggered and agent-to-agent workflows. Strong written and verbal communication, including the ability to present AI risks, trade-offs, and recommendations to senior stakeholders. Responsibilities Architect multi-agent systems end-to-end : decompose business workflows into specialized agents (routing, retrieval, domain reasoning, action execution, review/critique), choose the right orchestration topology per use case, and define agent contracts, handoff protocols, and shared memory/state. Design and build customer-facing voice + chat agent teams covering the full public website capability surface: inventory search and comparison, finance/payment estimation, trade-in valuation, offer discovery, appointment scheduling, and lead capture/qualification — with low-latency realtime voice, streaming chat, graceful fallback, and human handoff. Ground all agent responses in live business data with strict accuracy guarantees — no hallucinated prices, VINs, or offers — and implement brand-safe guardrails for public surfaces (prompt injection, jailbreaks, off-topic containment, PII handling). Build internal multi-agent workflows that operate backend business platforms conversationally and autonomously: campaigns, lead management, offers, payments, reporting, forecasting, and email/SMS marketing — following the insight → recommendation → approved action pattern with specialist agents collaborating under an orchestrator. Expose business capabilities as MCP tools with role-based permissions, audit logging, and approval policies scaled to action risk (read vs. draft vs. send vs. pay); enforce least-privilege tool access per agent. Build natural-language analytics: NL-to-SQL/semantic-layer querying, automated forecasting summaries, and scheduled executive digests. Own evaluation end-to-end: golden datasets per use case, offline regression suites, multi-agent trajectory and delegation evals , eval gates in CI/CD, A/B testing, and production quality monitoring (containment, conversion, CSAT). Establish LLMOps/AgentOps practices: prompt and agent versioning, per-agent model routing and semantic caching for cost control, token/latency budgets across agent chains, and observability dashboards with cross-agent distributed tracing. Lead root-cause analysis for agent failures (bad tool calls, wrong delegation, runaway loops, hallucinations, unsafe outputs) with durable systemic fixes and new guardrails. Partner with Product Owners, Architects, and platform teams to translate business workflows into agent capabilities and clear delivery plans; mentor engineers on multi-agent patterns, context engineering, and evaluation discipline; report AI system health, risks, and trends to leadership with supporting data.