Lead Agentic AI Engineer

About the Role:

We are looking for an 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/harness engineering, APIs, and enterprise systems to build intelligent assistants that can reason, use tools, execute actions, and validate results.

The ideal candidate has hands-on experience building agentic workflows beyond simple chatbots or PoCs, with strong software engineering skills and experience taking AI solutions into production.

Key Responsibilities:

1. 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.

1. Context & Harness Engineering

• Design and implement context engineering strategies to 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.

1. MCP & Tool Integration

• Design and develop Model Context Protocol (MCP) servers and tools to 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 allow agents to perform actions rather than simply generate responses.

1. 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.

1. 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 agent 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.

1. 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 supporting 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.

1. 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…