Lead AI/ML Engineer - R01572495

Lead AI/ML Engineer

• Design, build, and deploy AI agents and multi-agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.
• Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.
• Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision-making.
• Build production-ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
• Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments.
• Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost, applying advanced techniques and metrics.
• Collaborate with cross-functional teams to identify AI opportunities, rapidly prototype solutions, and iterate based on user feedback.
• Contribute to internal best practices around agent architecture, prompting, RAG, model selection, evaluation, and AI engineering standards.

• Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
• Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)
• LLM application development and prompt engineering
• Enterprise RAG architectures and vector search technologies
• Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs
• Python programming for AI/ML solutions
• ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras)
• AI workflow orchestration tools (KubeFlow, BentoML)
• Model evaluation and observability tools (Evidently AI, Great Expectations)
• AI security and governance on Azure

• Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
• Proficiency in R and R Studio for statistical computing
• Knowledge of SAS or SPSS for advanced statistical modeling
• Familiarity with microservices, event-driven architectures, and enterprise integrations
• Expertise in developing reusable agent frameworks, prompt libraries, and deployment patterns

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
• Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)
• Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)