Lead / Manager Agentic AI Engineer - Claude Code & Codex

About this role

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Skills you'll use

What you'll do

• Agentic AI Development & Orchestration - Design and develop LLM-powered autonomous and semi-autonomous agents capable of executing complex, multi-step workflows. Build agent workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies. Implement planning, task decomposition, tool selection, execution, observation, retry, and validation loops. Develop agents that can interact with enterprise applications, APIs, databases, and developer tools through natural language.
• Context and Harness Engineering:  Design and implement context engineering strategies that provide agents with the right instructions, task context, application state, tools, and relevant information at the right time. Develop AI agent harnesses that manage agent state, tool access, permissions, execution workflows, guardrails, retries, and verification. Engineer repository and application context for AI coding agents such as Claude Code, OpenAI Codex, or similar platforms. Develop effective agent instructions, project context, coding guidelines, workflows, and automated verification mechanisms to improve agent reliability and developer productivity. Optimize context usage to reduce unnecessary token consumption, latency, and LLM costs.
• MCP & Tool Integration:  Design and develop Model Context Protocol (MCP) servers and tools that enable agents to interact with enterprise applications and services. Integrate agents with Git, GitHub/GitLab, Artifactory, Slack, databases, APIs, CI/CD platforms, and other enterprise tools. Build secure tool-calling mechanisms with appropriate authentication, authorization, permissions, and human approval workflows. Develop reusable tools that enable agents to perform actions rather than simply generate responses.
• LLM & Generative AI Engineering:  Integrate and orchestrate LLMs for reasoning, planning, content generation, code generation, and task execution. Work with commercial and open-source LLMs and select appropriate models based on quality, latency, cost, and task complexity. Implement prompt engineering and advanced context management strategies. Apply techniques such as structured outputs, function/tool calling, model routing, and model fallback strategies. Implement RAG where required, including document retrieval, embeddings, vector databases, reranking, and grounding.
• Agent Evaluation & Reliability:  Design evaluation frameworks to measure agent task completion, tool-call accuracy, response quality, hallucination, reliability, and business outcomes. Implement LLM-as-a-Judge and automated evaluation pipelines for agentic and GenAI applications. Build regression testing and validation workflows for agent behavior. Implement guardrails, error handling, retry mechanisms, and human-in-the-loop controls for high-risk actions.
• Production Engineering & Deployment:  Develop production-grade AI services using Python and FastAPI or similar frameworks. Deploy and operate agentic applications in cloud and enterprise environments. Design scalable architectures that support concurrent users, long-running agent workflows, and complex tool execution. Implement caching, model/inference optimization, asynchronous processing, parallel execution, and cost optimization strategies. Integrate AI applications with CI/CD, monitoring, logging, tracing, and observability platforms.
• Enterprise AI Solutions : Translate complex business and product requirements into agentic AI solutions with measurable business impact. Work closely with product managers, software engineers, data scientists, architects, and business stakeholders. Build solutions that move beyond prototypes into scalable, secure, production-ready enterprise applications.
• 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.

Who can apply

• 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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