Senior Agentic AI Engineer

Role Overview: As a Senior Agentic AI Engineer at GDB/X in the Intelligent Automation Product Team, you will have the opportunity to lead the development of core components, build complex agents, integrate data & AI, optimize performance, mentor junior engineers, and develop tools compliant with Model Context Protocol (MCP). You will play a crucial role in shaping the future of BASF's digital landscape and contributing to projects that drive sustainability and make a real impact. Key Responsibilities: - Engineer Core Components: Lead the development of the orchestration service and Agent-to-Agent (A2A) bus by writing high-performance Python code that can route thousands of requests per minute. - Build Complex Agents: Implement "Horizon 3/4" autonomous agents using LangGraph or AutoGen hosted on AKS, such as self-correcting coding agents and complex supply chain optimizers. - Integrate Data & AI: Develop robust integrations between Databricks (Unity Catalog) and the agent runtime to ensure access to real-time, governed business data. - Optimize Performance: Address challenging problems in agentic AI, including latency reduction, token cost optimization, and managing distributed state across long-running workflows. - Mentor & Review: Conduct code reviews for junior engineers and divisional teams to ensure adherence to Zero-Trust security practices and software engineering standards. - Develop Tools (MCP): Build and maintain a library of MCP compliant tools to facilitate interactions with enterprise APIs like SAP, ServiceNow, and others. Qualifications: - Engineering Mastery: Proficient in writing clean, testable, production-grade Python (FastAPI, Pydantic) with familiarity in async/await patterns and concurrency. - Container Native: Experienced in deploying and debugging complex applications on Kubernetes (AKS) beyond local Docker usage. - Agentic Frameworks: Deep, hands-on experience with LangChain, LangGraph, or Semantic Kernel, including debugging looping agents and managing conversation memory effectively. - Azure & Databricks: Practical experience with Azure AI Foundry for model serving and Databricks for data engineering. - Problem Solver: Enjoy debugging non-deterministic systems and implementing creative solutions to enhance reliability, such as retry logic, output parsing, and verification steps. (Note: Omitted additional details of the company as it was not explicitly provided in the job description) Role Overview: As a Senior Agentic AI Engineer at GDB/X in the Intelligent Automation Product Team, you will have the opportunity to lead the development of core components, build complex agents, integrate data & AI, optimize performance, mentor junior engineers, and develop tools compliant with Model Context Protocol (MCP). You will play a crucial role in shaping the future of BASF's digital landscape and contributing to projects that drive sustainability and make a real impact. Key Responsibilities: - Engineer Core Components: Lead the development of the orchestration service and Agent-to-Agent (A2A) bus by writing high-performance Python code that can route thousands of requests per minute. - Build Complex Agents: Implement "Horizon 3/4" autonomous agents using LangGraph or AutoGen hosted on AKS, such as self-correcting coding agents and complex supply chain optimizers. - Integrate Data & AI: Develop robust integrations between Databricks (Unity Catalog) and the agent runtime to ensure access to real-time, governed business data. - Optimize Performance: Address challenging problems in agentic AI, including latency reduction, token cost optimization, and managing distributed state across long-running workflows. - Mentor & Review: Conduct code reviews for junior engineers and divisional teams to ensure adherence to Zero-Trust security practices and software engineering standards. - Develop Tools (MCP): Build and maintain a library of MCP compliant tools to facilitate interactions with enterprise APIs like SAP, ServiceNow, and others. Qualifications: - Engineering Mastery: Proficient in writing clean, testable, production-grade Python (FastAPI, Pydantic) with familiarity in async/await patterns and concurrency. - Container Native: Experienced in deploying and debugging complex applications on Kubernetes (AKS) beyond local Docker usage. - Agentic Frameworks: Deep, hands-on experience with LangChain, LangGraph, or Semantic Kernel, including debugging looping agents and managing conversation memory effectively. - Azure & Databricks: Practical experience with Azure AI Foundry for model serving and Databricks for data engineering. - Problem Solver: Enjoy debugging non-deterministic systems and implementing creative solutions to enhance reliability, such as retry logic, output parsing, and verification steps. (Note: Omitted additional details of the company as it was not explicitly provided in the job description)