Lead AI/ML Engineer

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)