Srivatsal Narayan

Founding AI engineer who took an enterprise AI platform from zero to org-wide adoption across 15+ business teams at MUFG - partnering directly with stakeholders to map use cases onto the platform, shipping pluggable AI agents with zero code changes needed to onboard a new data source, and presenting platform roadmap and impact directly to senior leadership. Track record of translating technical capability into measurable business outcomes: 40% faster data migrations, hundreds of self-served queries/day across 30,000+ dashboards.

AI / Data Engineer (Founding) at MUFG Global Service Private Limited (2024-11 – Present)

Sole architect. Designed, built, and shipped MUFG's internal AI platform end-to-end from scratch.

• AI Marketplace (0 → org-wide adoption): Independently designed and built MUFG's internal AI Marketplace - containerised, pluggable AI agents (RAG, NL2SQL, classifiers, summarisers) with metadata-driven routing and dynamic agent selection. Drove adoption across 15+ business teams by working directly with each team to map their use case onto the platform.
• Executive Engagement & Standards: Presented platform architecture and engineering roadmap regularly to senior MUFG leadership; defined engineering standards adopted across the AI team.
• Conversational Data Catalog + NL2SQL: Designed a metadata-aware NL2SQL system serving 30,000+ enterprise dashboards at hundreds of queries/day, letting business users self-serve data questions in plain English. Enforced credential-propagated two-tier security so adoption didn't compromise governance - a Collibra-grade backbone built in-house, avoiding a costly commercial license.
• DB2 → Snowflake Migration Platform: Architected a migration suite spanning 5 data warehouses and 100,000+ columns; cut migration timelines by 40%, directly reducing project cost and time-to-value for stakeholders.
• Pluggable RAG Orchestration Framework: Built a runtime-swappable RAG framework so new enterprise data sources could be onboarded with zero code changes - reducing the lead time between a business team's request and a working AI agent.

ML Research Intern at India–Taiwan Collaborative Lab (2024-07 – 2024-09)

• Designed CT lesion segmentation pipelines achieving 0.6 Dice score; improved accuracy 15% through systematic CNN architecture benchmarking.

iOS Developer Intern at Infosys (2024-04 – 2024-05)

• Delivered a Hospital Management System in 3 weeks; agile practices reduced delivery time by 25% and improved operational efficiency by 40%.

Machine Learning Intern at CSIR (2024-02 – 2024-06)

• Built UNet and YOLOv8 robotic-vision pipelines improving arm accuracy by 18%; CNN benchmarking drove 22% performance gains.
• Research contributed directly to two peer-reviewed, IEEE-indexed publications.

Internship - ML · Predictive Modelling at Apollo Tyres

Tyre Burst Prediction

• Built ML models preventing 30% of tyre burst incidents and reducing quality testing costs by 20%.

B.Tech – SRM Institute of Science and Technology (2021 – 2025)