Lead / Manager Agentic AI Engineer - Claude Code & Codex

About this role

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Skills you'll use

What you'll do

• We are looking for an Lead or a Manager, Agentic AI Engineer to design, build, and deploy production-grade AI agents capable of executing complex, multi-step workflows through natural language interactions.
• The role will focus on integrating LLMs, agent orchestration frameworks, MCP tools, AI coding agents, context and harness engineering, APIs, and enterprise systems to build intelligent assistants that can reason, use tools, execute actions, and validate results.
• The ideal candidate will have hands-on experience building agentic workflows beyond simple chatbots or proof-of-concepts (PoCs), along with strong software engineering skills and experience taking AI solutions into production.

Who can apply

• 5–10 years of experience in Data Science or AI/ML or Software Engineer out of which atleast 2 years in Generative AI and Agentic AI, with hands-on experience building production-grade AI applications and agentic systems.
• Strong development skills in Python and experience building APIs and AI services using FastAPI or similar frameworks, along with experience in context engineering and AI agent harness engineering, including agent instructions, application/task context, permissions, guardrails, retries, validation, automated verification, and context optimization.
• Experience with AI coding agents such as Claude Code, OpenAI Codex, or similar platforms, including repository context, agent instructions, automated testing, coding workflows, and verification.
• Hands-on experience with A2A, MCP (Model Context Protocol), including developing or integrating MCP servers and connecting agents with enterprise tools, APIs, databases, and SaaS platforms.
• Hands-on experience with LLMs, agent orchestration, and multi-step/multi-agent workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent technologies.
• Strong understanding of agent architecture, tool/function calling, planning, task decomposition, state/memory management, dynamic tool invocation, and workflow orchestration.
• Experience with RAG, embeddings, vector databases, semantic search, chunking, reranking, and grounding, with knowledge of LLM-as-a-Judge and automated GenAI evaluation.
• Strong software engineering and cloud experience, including Git, CI/CD, Docker, databases/SQL, AWS/Azure/GCP, with familiarity with asynchronous programming, parallel processing, observability, and distributed systems.
• Strong understanding of LLM performance and production optimization, including hallucination mitigation, context windows, token usage, latency, caching, cost optimization, and monitoring.
• Demonstrated ability to build secure, scalable, production-ready AI solutions with measurable business impact beyond POCs.

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