Where I've put hours in.
Three roles, oldest at the bottom. Each line is a specific thing I owned, not a job description.
- Mar 2024—PresentFull-time
Data Engineer
Accelerize 360·Hyderabad
- Shipped an autonomous AI agent on the Claude API (Anthropic) that plans, drafts, and refines presentation decks through multi-turn chat. Deck creation dropped from 2+ hours to ~30 minutes.
- Built a text-to-SQL tool on Snowflake Cortex so marketers segment customers in plain English, no SQL needed. Marketing costs fell 70%.
- Deployed a RAG support chatbot on Snowflake Cortex that answers from internal docs, using hybrid retrieval and reranking. Hit ~95% Recall@5 and ~90% Precision@5.
- Provisioned AWS infrastructure (S3, Lambda, Glue, Kinesis, MSK) with Terraform and rebuilt batch jobs as serverless microservices. Runtime went from 3+ hours to under 15 minutes — 10x faster.
- Engineered batch and streaming ETL/ELT pipelines from PostgreSQL, MongoDB, Kafka (AWS MSK), Kinesis, and APIs into Snowflake's Medallion layers, then tuned them: 70%+ faster, 5x the data, 40%+ cheaper.
- Led 4+ engineers as Primary Consultant, turning client problems into working AI and data products from idea to launch.
- May 2023—Jul 2023Internship
Data Science Intern
UIDAI Technology Centre·Bangalore
- Trained and evaluated ML and deep-learning models for biometric fraud detection.
- Ran CNN-based fingerprint experiments to sharpen anomaly detection, tracking precision, recall, and false-acceptance rate.
- Wrote end-to-end ML workflows in Python with TensorFlow, OpenCV, and scikit-learn.
- Dec 2022—Aug 2023Internship
Research Intern
Samsung Research Institute·Remote · Bangalore
- Developed deep-learning pipelines for pose estimation and keypoint detection using CNN models.
- Created annotation, labeling, and validation tooling for the research team.
- Turned experimental research models into reusable ML components, and cleaned training data to reduce bias.