Lead Agentic AI Engineer – Python, LangGraph & Azure
Dallas TX (Remote)
Role Summary
We are 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.
This role is intended for a senior technical leader who combines deep hands-on engineering with solution ownership. The Lead Agentic AI Engineer will define the 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.
Required Qualifications
Strong depth in Python, LangGraph, agentic application development, workflow orchestration, and technical leadership is required. Experience with the broader Azure, observability, DevSecOps, and integration stack may come through direct implementation or close collaboration with specialized engineering teams.
- 12+ years of overall software/application engineering experience.
- 5+ years of strong hands-on Python development.
- 2+ years of hands-on Generative AI / LLM application development.
- 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.
- Demonstrated technical leadership experience.
Required Technical Skills
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Skill Area
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Expected Level
|
|
Python
|
Expert
|
|
LangGraph
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Expert / Strong Hands-on
|
|
Agentic AI Application Development
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Expert
|
|
Multi-Agent Orchestration
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Strong
|
|
Agent State & Workflow Management
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Strong
|
|
Tool / Function Calling
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Strong
|
|
REST API Integration
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Strong
|
|
Azure OpenAI / Enterprise LLM Integration
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Strong
|
|
Human-in-the-Loop Workflows
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Strong
|
|
Event-Driven Architecture
|
Strong
|
|
Application Testing
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Strong
|
|
AI Evaluation / Agent Testing
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Strong
|
|
Observability / Troubleshooting
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Strong
|
|
Cloud Application Development
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Strong
|
|
Technical Leadership
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Strong
|
Key Responsibilities
The Lead Agentic AI Engineer will directly own the core Agentic AI engineering and technical leadership responsibilities for the solution. The role will also contribute to, influence, or support adjacent areas such as cloud platform, integration, observability, DevSecOps, security, UI, testing, and production readiness in collaboration with the respective engineering teams.
The expectation is not for this role to independently own every technology or platform component, but to ensure that the Agentic AI solution integrates effectively across these areas.
- 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 the 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.
Preferred Qualifications
Experience with one or more of the following technologies or domains is beneficial; expertise across the entire stack is not required.
- Azure Container Apps
- Azure Functions
- MongoDB
- Langfuse
- Application Insights
- Azure Monitor
- Dynatrace
- GitHub Enterprise
- Azure DevOps
- JFrog Artifactory
- Entra ID
- Okta
- Enterprise document-processing solutions
- Insurance or financial-services platforms
- Regulated enterprise environments
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