I bring over 9 years of experience in data engineering, machine learning, and analytics, specializing in architecting and delivering scalable, enterprise-grade data solutions across cloud platforms. I have successfully implemented end-to-end data pipelines and AI-driven systems for both structured and unstructured data, enabling data-driven decision-making at scale. My expertise spans data architecture, advanced data modeling, Delta Lake optimization, and presenting impactful analytical insights to solve complex business problems. I have led large data engineering initiatives for global clients across supply chain, pharma, and healthcare domains, working with top firms including Fractal Analytics, ZS Associates, and Accenture. I’m also actively engaged in building agentic AI solutions using frameworks like LangChain and LangGraph, with a focus on automation, intelligent orchestration, and contextual data applications. Technical Skills: Cloud & DevOps: Microsoft Azure Certified (DP-100, DP-200, DP-201, DP-900, AI-900, AZ-900). Hands-on with Azure Data Factory, Databricks, ADLS Gen2, ADLA, Blob Storage, Azure Functions, SSIS, Azure Synapse, Docker, Kubernetes, GitHub Actions, CI/CD, and DevOps automation. Architecture & Large-Scale Data Solutions: Proven experience in architecting and managing large-scale, enterprise data platforms with a focus on performance, scalability, and cost-efficiency. Agentic AI & Automation: Practical implementation of LangChain and LangGraph to build intelligent data agents and orchestration workflows. Programming & Big Data: Skilled in Python, SQL, and PySpark; experienced in building resilient ETL/ELT pipelines and streaming solutions. Databases: Proficient in MySQL, PostgreSQL, SQL Server. Machine Learning & MLOps: Experience with supervised/unsupervised ML models, Azure ML, and MLflow for scalable model training and deployment. BI & Data Visualization: Proficient in QlikView, Qlik Sense, and NPrinting for building dashboards and automated reporting systems.