resume

Professional summary

Machine Learning Engineer with 3 years of experience designing, building, and deploying models in production for cross-functional teams. Covers the full path from data preprocessing and feature engineering to evaluation, fine-tuning, and continuous delivery on AWS with Docker, Kubernetes, and automated CI/CD. Currently completing an M.S. in Data Science at Indiana University (May 2026).

Experience

Adobe

Machine Learning Engineer Intern

May 2025 – August 2025, San Jose, California

• Built an LLM-as-Judge evaluation harness with LiteLLM to benchmark Gemini 2.5 Pro, GPT-4.1, and Claude 4.5 on multimodal chart-quality tasks, raising offline F1 from 79% to 92% by redesigning the scoring rubric against a human-labeled reference set and tracking every run in Weights & Biases.

• Shipped a LangGraph agent over Model Context Protocol and Adobe PDF Spaces that cut evaluation feedback from 48 hours to 30 minutes, with LangSmith tracing and a human approval gate on every generated plan.

S&P Global

Machine Learning Engineer II

November 2022 – August 2024, Hyderabad, India

• Fine-tuned and served LayoutLMv3 for token classification over financial filings on AWS EC2, versioning datasets and model artifacts with DVC and MLflow and promoting releases through CI/CD so training runs stayed reproducible and rollbacks were safe.

• Deployed Llama 2 on SageMaker and AWS Lambda with chain-of-thought prompt templates for page-level extraction, removing about 20 minutes of manual data collection per analyst task.

• Designed and deployed a T5 question generation and answering API on AWS EC2 that resolved current-year figures from long unstructured filings inside analyst-facing latency budgets.

• Owned the team MLOps workflow with DVC, MLflow, GitHub Actions, and Docker, and replaced a paid third-party parser with an in-house Java named-group regex service that saved roughly 10 minutes of analyst time per filing.

American Express

Data Scientist I

December 2021 – November 2022, Gurgaon, India

• Built and containerized VIBE, a production call-quality scoring model combining XGBoost with BERT embeddings, used as the system of record for annual representative incentive decisions.

• Deployed a three-tier complaint risk classifier with Naive Bayes, TF-IDF, and isotonic probability calibration on AWS EC2 so routing thresholds could be set on calibrated risk.

• Served an LSTM intent model with Flask on AWS EC2 that mapped call transcripts to demand types and prioritized incoming contacts, with PySpark feature generation over the transcript corpus.

Education

Indiana University

Master of Science in Data Science

August 2024 – May 2026, Bloomington, Indiana

Rajarambapu Institute of Technology

Bachelor of Technology in Electronics & Tele-Communications

August 2017 – May 2021, Sangli, India

Skills

Languages: Python, SQL, Java, C++, CUDA C++, PySpark

Machine learning: Deep learning, NLP, transformers, LLMs, fine-tuning (LoRA, PEFT), quantization (AWQ, GPTQ), RAG, agentic workflows, model evaluation, feature engineering, probability calibration, XGBoost

Frameworks and serving: PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn, LangChain, LangGraph, LiteLLM, vLLM, FastAPI, Flask, Model Context Protocol

Cloud and MLOps: AWS (SageMaker, EC2, Lambda, S3), Docker, Kubernetes, ArgoCD, GitHub Actions, MLflow, DVC, Weights & Biases, LangSmith, Nsight Compute