Agentic AI- Analyst

• Design, develop, and deploy enterprise AI agents using AI Refinery, LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, and custom agentic architectures.
• Build intelligent multi-agent systems capable of reasoning, planning, task decomposition, collaboration, and autonomous execution.
• Develop reusable agent orchestration frameworks, AI accelerators, and enterprise AI components.
• Design and implement Model Context Protocol (MCP) servers and integrations enabling seamless communication between AI agents and enterprise applications.
• Design Human-in-the-Loop (HITL) workflows where business approvals or validations are required.
• Build AI copilots, autonomous agents, and workflow automation solutions for enterprise use cases.

LLM Engineering & AI Model Development

• Build, fine-tune, evaluate, and optimize Large Language Models (LLMs) for enterprise use cases.
• Develop Generative AI applications using advanced prompt engineering, structured outputs, function calling, and tool calling.
• Evaluate AI models across accuracy, latency, grounding quality, safety, hallucination rates, and business KPIs.
• Optimize prompts, agent workflows, and reasoning strategies to improve reliability and performance.
• Implement model evaluation pipelines and continuous improvement mechanisms.

Retrieval-Augmented Generation (RAG)

• Design and implement enterprise RAG solutions using Azure AI Search, vector databases, semantic search, and knowledge retrieval techniques.
• Develop document ingestion, chunking, metadata enrichment, indexing, and retrieval pipelines.
• Optimize grounding strategies and retrieval quality to improve AI response accuracy.
• Build scalable enterprise knowledge management solutions powered by LLMs.

Enterprise AI Integration

• Integrate AI agents with ServiceNow, Microsoft Graph API, Microsoft Teams, Splunk, Azure Functions, Azure SQL, and REST APIs.
• Build secure MCP integrations enabling AI agents to interact with enterprise systems.
• Develop scalable enterprise automation workflows leveraging AI agent orchestration.
• Design resilient API integrations with security, authentication, logging, retries, and error handling.

AI Governance, Evaluation & Observability

• Implement AI guardrails, Responsible AI principles, governance controls, and compliance standards.
• Design evaluation frameworks for measuring AI quality, grounding, latency, hallucination rates, and business outcomes.
• Build observability dashboards to monitor AI agent execution, token consumption, workflow performance, cost, and operational health.
• Ensure enterprise AI applications meet organizational security, governance, and compliance requirements.

Solution Engineering & Deployment

• Collaborate with architects, product owners, and engineering teams to design and deliver scalable cloud-native AI solutions.
• Deploy AI applications using Azure AI Foundry, Azure OpenAI, Azure Functions, and enterprise cloud platforms.
• Participate in architecture, design, code reviews, testing, deployment, and production support.
• Implement CI/CD pipelines and MLOps practices for AI solution lifecycle management.

Innovation & Knowledge Sharing

• Stay updated with advancements in Generative AI, Agentic AI, MCP, LLMs, and enterprise AI technologies.
• Develop reusable frameworks, accelerators, templates, and best practices.

Create technical documentation and mentor junior team members