Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands-on expertise in agentic AI, LLMs, and Azure-based solutions. Key Responsibilities:
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.Required Skills:
Hands-on experience with Azure AI services, Azure OpenAI, and Azure AI FoundryExpertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)LLM application development and prompt engineeringEnterprise RAG architectures and vector search technologiesIntegration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIsPython programming for AI/ML solutionsML 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 AzurePreferred Skills:
Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure StorageProficiency in R and R Studio for statistical computingKnowledge of SAS or SPSS for advanced statistical modelingFamiliarity with microservices, event-driven architectures, and enterprise integrationsExpertise in developing reusable agent frameworks, prompt libraries, and deployment patternsDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related disciplineCertification 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)