About the role
Job requirements
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 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
Preferred Skills
- 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
Desired Qualifications
- 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)