AI/ML Engineer
I’m an AI/ML Engineer, currently pursuing a B.Tech in Computer Science and Engineering with a specialization in Artificial Intelligence, Machine Learning, and Deep Learning. Skilled in Python, SQL, HTML, and CSS, with a solid understanding of systems and modern software development practices, along with a foundational knowledge of C++. I thrive at the intersection of artificial intelligence, machine learning, cloud computing, and modern software engineering. I specialize in building end-to-end ML pipelines, scalable backend systems, and cloud-integrated applications that blend intelligence with performance. My technical stack includes Docker, Azure, CI/CD pipelines, data pipelines, and workflow automation tools, enabling me to design and deploy solutions that learn, adapt, and scale efficiently. Passionate about clean engineering, data-driven problem solving, and continuous innovation, I actively explore emerging technologies in Generative AI, cloud-native architectures, and autonomous ML workflows with the goal of building systems that not only work, but also think, evolve, and create impact.
Mastered enterprise-level Git and GitHub workflows, repository initialization, branching strategies (Git Flow), pull request reviews, and CI/CD setup via GitHub Actions. Implemented version control best practices and collaborative issue tracking for scalable software development.
Gained expertise in leveraging AI-powered code completion to enhance productivity, code quality, and collaboration. Mastered Copilot’s integration within IDEs, prompt engineering for accurate code suggestions, secure coding practices, and workflow optimization through GitHub Copilot for Individuals and Business.
Executed data wrangling, transformation, and visualization using Excel (PivotTables, XLOOKUP) and SQL (JOIN, GROUP BY, AGGREGATE). Designed analytical dashboards and optimized data pipelines to deliver insight-driven reporting and performance monitoring.
Developed intelligent applications leveraging Azure AI APIs for Vision, Language, and Search. Implemented OCR, sentiment analysis, entity recognition, and translation models with secured Azure authentication and responsible AI integration.
Implemented end-to-end ML pipelines using Scikit-learn - preprocessing, model training, and hyperparameter tuning. Applied regression and classification algorithms with metrics such as F1-Score, ROC-AUC, and cross-validation for performance optimization.
Engineered and deployed an innovative prototype demonstrating applied AI and data-driven problem-solving. Led requirements gathering, implementation, benchmarking, and technical presentation, earning recognition for innovation and execution excellence.
Studied distributed data processing with Hadoop and Apache Spark (RDDs, DataFrames). Gained expertise in partitioning, shuffling, fault tolerance, and optimization techniques for scalable big data analytics and cluster-based computation.
Performed comprehensive data analysis using advanced Excel functions. Power Query, PivotTables, and What-If Analysis. Built automated KPI dashboards to visualize trends, patterns, and business metrics with data-driven accuracy.
Conducted real-world data analytics using Python (NumPy, Pandas, Matplotlib, Seaborn). Focused on data wrangling, feature engineering, and visualization to generate actionable insights and reproducible analytical reports.
Gained practical understanding of MLOps fundamentals, dataset versioning, experiment tracking, model packaging, CI/CD pipelines, and monitoring for production-level ML systems ensuring reproducibility and governance.
Strengthened model evaluation and deployment workflows, implementing precision/recall optimization, bias detection, drift monitoring, and REST-based inference systems with robust logging and observability frameworks.
Built and optimized relational queries using subqueries, window functions, and complex JOINs. Enhanced query performance with indexing and execution plan analysis to ensure scalable and efficient database operations.
Configured and optimized GA4 properties, events, and conversion tracking. Implemented UTM tagging, funnel visualization, and audience segmentation for data-driven campaign performance measurement and optimization.
Contributed to data-driven community initiatives through structured reporting, event coordination, and analytics using Excel and Google Sheets. Focused on measurable outreach impact, documentation clarity, and collaborative workflow.
CureHelp+
An AI‑assisted health companion offering preliminary insights, symptom‑based guidance, and smarter access to care.
IntervBot
Practice interviews with AI‑generated questions and structured feedback to build confidence and improve answers.
TinyURL
Blazing‑fast link toolkit: shorten and unshorten URLs, and generate QR codes for effortless sharing anywhere.
PredicTrade
AI‑driven market forecasting that turns historical trends and live signals into clear, actionable insights for smarter investing.
Lead Validation Suite
Cleans and verifies lead data to remove junk and keep only accurate, high‑quality contacts in your pipeline.
Personal Website
A clean, responsive portfolio showcasing projects, writing, and skills across AI, ML, and full‑stack development.
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New project in development
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