Greater Bengaluru Area
Completed Master of Technology (MTech) in Artificial Intelligence at the prestigious Indian Institute of Technology, Madras, and currently contributing as a Senior Member of Technical Staff at Gracenote, a Nielsen company. Core competencies include data engineering, machine learning, and MLOps practices, with a strong foundation in Python, Redshift, and AWS. Academic and professional experiences are complemented by a Bachelor of Technology in Information Technology from Kalyani Government Engineering College. At Gracenote, collaborates on cutting-edge data-driven solutions, leveraging expertise in data pipelines, machine learning modeling, and scalable architectures to deliver impactful outcomes. Passionate about fostering innovation and driving efficiency, they bring a collaborative approach to solving complex challenges. Dedicated to creating sustainable solutions that align with organizational growth and promote a culture of teamwork and progress.
Mentoring Students for IEEE SMC KGEC chapter.
• Architected CCP data platform end-to-end (ingestion → validation → feature pipelines) with SOLID design and near-zero rework. • Built ODR (On-Demand Refresh): one-click ad-hoc ETL → 0 orchestrator failures over 8+ months; 0 refresh tickets; faster post-fix re-triggers. • Built FPM (Fail-Proof Mechanism): Excel→Parquet + dynamic Redshift DDL → incidents reduced from 15+ to ~2 per month (~87%↓); ~80% fewer support tickets. • Shipped 4 production ML models across distinct business questions: Analyst-action recommendation (classification) with SHAP waterfall explanations to analysts. • Prototyped natural-language Q&A over Tableau (LangChain) to speed self-serve analytics. • Established MLOps practices: Redshift feature stores, experiment tracking, approvals, post-deploy monitoring, BA feedback loops; internal Flask APIs; CI/CD on Jenkins/Bitbucket. Stack: Python, scikit-learn/XGBoost, SHAP, SQL (Redshift/Postgres), Flask, Tableau, LangChain, AWS (S3/EC2/RDS/Redshift), Jenkins/Bitbucket, MLOps.
• Productionized “Data Sentry” for onboarding/offboarding (DB access + AD groups) across AWS/Azure/GCP on Lambda + S3 with OAuth/Microsoft Graph. • Delivered 10k+ lines of Redshift SQL in ~3 months → zero defects to production. • Enhanced self-service access for platform teams, significantly reducing operational toil and expediting SQL delivery. Stack: Python/Pandas, AWS (Lambda/S3), OAuth/Microsoft Graph, Redshift SQL, Git/Jenkins.
• Ran production ETL on AWS (Redshift/RDS/EC2/S3) orchestrated with Control-M; authored complex SQL for analytics and reporting. • Built S3-based cross-database transfers and dirty-data filters → eliminated delimiter-related ETL failures. • Tuned legacy Redshift views → ~3× faster runtimes and lower compute cost. • Drove 50+ production releases with <2% rework; SDET on 20+ SIT cycles with 0% rework. • Mentored peers on SQL patterns, release hygiene, and on-call runbooks. Stack: Python, SQL/Redshift, AWS (S3/EC2/RDS), Control-M, Jenkins, Bitbucket/Git.
I played a key role in advancing Param.ai's hiring automation efforts through the development of a sophisticated similarity matching algorithm. • Leveraged advanced AI techniques to enhance candidate-job fit, improving recruitment efficiency. • Utilized Jaccard similarity and cosine distances on a custom-trained Word2Vec model to optimize matching processes. • Actively contributed to the startup's vision of transforming the hiring landscape for top candidates.