M.S. Applied Data Intelligence student at San Jose State University, graduating Dec 2026. 4+ years across software testing, data operations, and ML — building reliable systems from QA pipelines to AI workflows.
// Who I am
I'm a versatile engineer with 4+ years of experience spanning software testing, data operations, and ML at Tata Consultancy Services and AVM IT Solutions — from validating production systems to building end-to-end pipelines and ML models.
Currently completing my M.S. in Applied Data Intelligence at SJSU, I'm diving deep into ML systems, multimodal AI, and production-grade data engineering. I'm passionate about building systems that are not just technically sound — but genuinely useful.
I'm actively seeking full-time roles in Data Science, ML Engineering, Data Engineering, or Software/QA Engineering.
M.S. Applied Data Intelligence
San Jose State University · San Jose, CA
Aug 2024 – Dec 2026
B.E. Computer Science & Engineering
Sri Sairam College of Engineering, VTU · Bangalore, India
Aug 2017 – Aug 2021
// Where I've worked
Building data systems that cut reporting cycles, improve accuracy, and drive business decisions.
Tata Consultancy Services · Bangalore, India
Dec 2022 – Jul 2024
Tata Consultancy Services · Bangalore, India
Nov 2021 – Dec 2022
AVM IT Solutions · Bangalore, India
Jan 2020 – Jun 2020
// What I've built
End-to-end AI/ML and data engineering systems — from research to production.
Full-stack AI permit pre-submission assistant serving 15–20 U.S. cities. A 5-agent RAG pipeline with retrieval, completeness assessment, guardrail, and response-generation agents — with IBM watsonx.governance monitoring to prevent hallucinations.
Natural-language video retrieval system using CLIP, BLIP, Mistral-7B, and Flamingo-inspired VLMs. Enables timestamp-level event search across video corpora. Fine-tuned CLIP ViT-B/32 on 670 MSVD videos against 27,763 text queries.
Machine learning project analyzing air quality trends to inform smarter urban planning decisions. Applies EDA, feature engineering, and predictive modeling to environmental datasets for actionable city-level insights.
End-to-end data warehouse and ingestion pipeline covering ETL design, transformation logic, and scalable storage. Demonstrates production-style data engineering patterns from raw ingestion to analytics-ready layers.
Machine learning-based stock price prediction system. Applies time series modeling and regression techniques to financial data, with feature engineering on historical price signals to forecast market movements.
Comparative study of ML classifiers on biometric datasets. Random Forest achieved the highest accuracy with a 6.33% improvement over baseline, validating feature engineering and model selection approaches on 2,000+ records.
// What I work with
A full-stack data toolkit — from raw pipelines to ML models to polished dashboards.
// Let's connect
M.S. Applied Data Intelligence student at SJSU, graduating Dec 2026. Actively seeking full-time roles in Data Science, ML Engineering, Data Engineering, and Software/QA Engineering. Let's talk!