• Agentic Engineering & Orchestration Workflow Design: Architect complex, multi-agent workflows using Microsoft AI tech stack. • Design, Develop and Deploy agents to handle loops, interruptions, and human-in-the-loop interventions. • Tool Use & Function Calling: Build reliable "tool layers" that allow LLMs to safely interact with internal APIs, databases, and third-party SaaS platforms (e.g., Salesforce, Workday, ServiceNow etc.). • State Management: Design persistence layers to manage agent memory, conversational history, and context windows efficiently. • Advanced Data & RAG Strategy Retrieval Pipelines: Build production-grade data retrieval and integration systems. • Optimize vector indexing, document chunking, and re-ranking algorithms to ensure high-precision context retrieval. • Data Quality: Collaborate with Data Engineers to curate "Golden Datasets" for agent consumption. • LLMOps, Evaluation & Quality Automated Evaluation: Build CI/CD pipelines for AI that include "LLM-as-a-Judge" testing. • Leverage frameworks to score agent outputs for accuracy, hallucination, and safety before deployment. • Observability: Instrument applications with tracing tools to visualize agent reasoning chains, monitor latency, and debug failures in production. • Cost Optimization: Monitor token usage and latency, optimizing prompt density and caching strategies to maintain high performance at sustainable costs. • Innovation & Collaboration: Prototyping to Production: Rapidly validate new ideas using state-of-the-art models, then refactor successful prototypes into maintainable, tested production code. • Standards Adoption: Stay ahead of the curve by evaluating emerging technologies to standardize agent connectivity.