Hello, I'm
Specializing in Machine Learning, NLP, Generative AI & Agentic AI. Proven ability to build scalable pipelines, derive actionable insights, and architect intelligent solutions from end-to-end.
I am a results-oriented Data Scientist with 5+ years of experience in developing and deploying cutting-edge machine learning solutions. My passion lies in solving complex problems using GenAI, Agentic AI, Model Context Protocol (MCP), A2A Protocol, Machine Learning, Natural Language Processing (NLP), and Computer Vision.
I thrive on building scalable models and productionizing AI applications using cloud platforms like AWS, Azure, and GCP. From conceptualizing Local RAG systems to orchestrating multi-AI agent workflows, I am dedicated to driving innovation and delivering tangible business impact.
Years Experience
AI Projects
Cloud Platforms
Electronics & Telecommunication
Thakur College of Engineering & Technology
CGPA: 9.04/10 - Gold Medalist for "IC the future" Competition
Driving advanced AI initiatives and building end-to-end data pipelines for scalable machine learning deployments.
Implemented a Local RAG System using a Mistral-7B quantized model, reducing assessment creation time by 2-3 man-days per course and improving productivity by 15%. Also developed the "assessment-creator" Python package published on PyPI.
Led the development of a No-Code ML Platform with integrated AutoML. Automated ML pipelines, achieving a 70% reduction in development time. Fine-tuned NLP models (BERT) for sentiment analysis achieving a 20% accuracy boost.
Managed large-scale SAP HANA databases, reducing data downtime by 20%. Developed Tableau BI dashboards tracked monthly incidents and improved SLA by 30%.
Architected an agentic Text-to-SQL chatbot utilizing stateful LangGraph workflows to translate natural language user queries into highly optimized SQL commands. Deployed on an AWS EC2 instance with a custom conversational UI, integrating schema pruning and execution validation for secure, real-time database querying against a sales dataset.
Architected an enterprise-grade Agentic RAG system on the Azure ecosystem, utilizing Azure OpenAI for intent routing and generation. Orchestrated LangChain agents with Azure AI Search for high-dimensional semantic retrieval, incorporating multi-turn memory management to deliver highly accurate, context-aware responses.
Designed a modular, scalable No-Code ML Platform with an AutoML core. Hosted on AWS microservices, the backend intelligently handles missing data imputation, hyperparameter tuning via Bayesian optimization, and model selection, seamlessly exposing operations via REST APIs to a dynamic drag-and-drop frontend.
Engineered a distributed multi-agent system using CrewAI and LangGraph to model complex workflows. Implemented specialized autonomous agents with stateful graph-based execution, dynamic tool calling for real-time pricing data, and iterative consensus mechanisms to generate highly optimized itineraries at scale.
Developed a robust ML intelligence layer for New Product Introduction (NPI), integrating Scikit-Learn predictive models into complex SQL data pipelines. Deployed via a continuous integration MLOps framework, leveraging historical feature sets to predict manufacturing bottlenecks and supply chain risks.
Built a voice-first conversational agent integrating real-time speech-to-text (STT) and text-to-speech (TTS) pipelines with a fine-tuned Mistral LLM backend. The system employs zero-shot entity extraction and robust dialogue management to handle unconstrained queries and multi-intent slot filling under noisy audio conditions.
Implemented an abstractive NLP pipeline utilizing the Pegasus transformer model, optimized for document-level understanding. Leveraged deep learning techniques including dynamic sequence padding and custom tokenization, evaluated via BERTScore and ROUGE to ensure semantic fidelity of the generated summaries.
Developed a predictive compensation analytics engine utilizing optimized Random Forest regressors. Ensured model transparency by integrating SHAP values to interpret global/local feature contributions, combined with a built-in rule-engine triggering a human-in-the-loop email escalation workflow for TA-CHRO.
Architected a scalable Model Context Protocol (MCP) server bridging Voice AI with the Compensation Fitment pipeline. Designed a low-latency stateful server handling audio streams, transcribing intents, and querying classical models seamlessly via structured JSON-RPC communication secured by a relational database backend.
I regularly host community sessions and have created comprehensive playlists on modern data science tools including LangGraph, Tableau, and Power BI.
15+ hours of comprehensive content covering agentic AI and LangGraph workflows.
Deep dive into business intelligence and interactive dashboard creation using Tableau.
Mastering data modeling, DAX, and end-to-end reporting with Power BI.
Comprehensive interview preparation series for Business Analysts, covering key concepts and real-world scenarios.