Job Summary:
Seeking a highly experienced, hands-on Lead Agentic AI Engineer to lead the design and development of a production-grade enterprise Agentic AI solution using Python, LangGraph, Azure OpenAI, and Azure cloud services. The solution supports complex policy-processing workflows through specialized Producer and Receiver agents, centralized orchestration, human-in-the-loop interactions, event-driven processing, enterprise system integration, persistent workflow state, observability, and production controls. The role combines deep hands-on engineering with solution ownership and technical leadership. The Lead Agentic AI Engineer will define agent architecture, establish engineering patterns, guide the development team, review critical code, resolve complex technical issues, and help ensure the solution is production-ready, scalable, secure, observable, and maintainable.

Key Responsibilities:
 Lead the design and development of production-grade Agentic AI applications using Python, LangGraph, and Azure OpenAI.
 Define and implement agent workflows, routing, tool calling, human-in-the-loop interactions, and multi-agent coordination.
 Provide technical leadership for Producer, Receiver, and Central Orchestration components, ensuring consistent patterns and reliable agent-to-agent workflows.
 Establish Python and LangGraph engineering standards, reusable components, coding practices, and technical design patterns.
 Remain hands-on with development, code reviews, debugging, complex workflow implementation, and resolution of critical engineering issues.
 Guide the appropriate use of LLM reasoning versus deterministic business logic.
 Lead the design of agent tools and integrations with enterprise APIs and business systems.
 Define approaches for agent state, persistence, checkpointing, retries, workflow recovery, and long-running business processes.
 Establish agent observability, evaluation, guardrails, and testing practices to support reliable production operation.
 Mentor AI engineers, review technical designs, support sprint planning, identify engineering risks, and drive technical readiness for releases.
 Collaborate with architecture, platform, DevOps, security, UI, integration, QA, and operations teams on cloud deployment, CI/CD, infrastructure, identity, monitoring, security controls, testing, and production support.
 Support integration of the Agentic AI solution with cloud platforms, enterprise systems, observability tools, security controls, and production environments.

Required Qualifications:
 12+ years of overall software/application engineering experience.
 5+ years of strong hands-on Python development experience.
 2+ years of hands-on Generative AI/LLM application development experience.
 Strong hands-on experience with LangGraph or comparable stateful graph-based agent orchestration frameworks.
 Proven experience building production-grade agent-based applications.
 Strong understanding of multi-agent systems, agent state, tool calling, human-in-the-loop workflows, checkpointing, retries, and recovery.
 Strong experience building and integrating REST APIs.
 Strong understanding of event-driven and asynchronous application patterns.
 Experience integrating LLMs with enterprise systems and APIs.
 Experience with Azure OpenAI or comparable enterprise LLM services.
 Experience with automated testing and production debugging.
 Experience designing secure and observable enterprise applications.
 Strong experience with Agentic AI application development and workflow orchestration.
 Strong experience with agent state and workflow management, tool/function calling, REST API integration, and human-in-the-loop workflows.
 Strong understanding of event-driven architecture, application testing, AI evaluation, agent testing, observability, and troubleshooting.
 Strong cloud application development experience.
 Demonstrated technical leadership experience.
 Ability to guide engineering teams, review technical designs and code, identify engineering risks, and drive technical readiness for production releases.

Preferred Qualifications:
 Experience with Azure Container Apps or Azure Functions.
 Experience with MongoDB.
 Experience with Langfuse, Application Insights, Azure Monitor, or Dynatrace.
 Experience with GitHub Enterprise, Azure DevOps, or JFrog Artifactory.
 Experience with Entra ID or Okta.
 Experience with enterprise document-processing solutions.
 Experience with insurance or financial-services platforms.
 Experience working in regulated enterprise environments.