I'm a Machine Learning Engineer at eBay, where I build LLM agents and GenAI systems that run in production at marketplace scale. My work spans agentic services — LLM-generated summaries, multimodal analysis across text and images, MCP tool servers — and the classic ML behind them, like gradient-boosted models trained on large-scale data. Before eBay, I worked on ML systems for the financial industry at FINRA, and built data platforms at Datava and Infosys. I hold an MS in Computer Science from the University at Buffalo (SUNY). I care about shipping AI systems that are measurable, safe, and actually used in production.
• LLM & Agentic Systems: Claude (Anthropic API), Azure OpenAI, Vertex AI, MCP (Model Context Protocol), agent orchestration, RAG, prompt engineering, LLM evals
• Machine Learning & NLP: PyTorch, TensorFlow, Hugging Face Transformers, XGBoost, spaCy, NLTK
• Programming Languages: Python, Java, SQL, Scala, Go, Bash
• Data & Distributed Systems: Spark, Hadoop, Hive, Kafka, Flink, ETL, Pandas, NumPy
• Cloud & CI/CD: AWS, Docker, Kubernetes, Terraform, Jenkins, Ansible
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I build production GenAI systems at eBay. My main focus is an LLM-powered service that turns complex, multimodal inputs — long text threads, images, OCR output — into structured summaries and signals, and exposes its capabilities to AI agents as tools over MCP. I also work on an internal multi-agent platform (agent registries, MCP server configuration, CI validation for agent packs) and build multi-stage XGBoost models on marketplace-scale data.
Built machine learning and AI systems addressing challenges in the financial industry.
Experienced Software Engineer proficient in Data Infrastructure contributing to the growth and success of fintech startup Datava.
Worked under the distinguished guidance of the renowned Prof. Dr. Bina Ramamurthy on Distributed Systems at SUNY Research Foundation, New York. Research Topic: A DeFi protocol that operates on a blockchain, enabling automated transactions between cryptocurrency tokens on the Ethereum network (Ropsten) without the need for traditional intermediaries.
As a Software Engineer, I've developed scalable backends in Python and Java, created efficient data pipelines using Spark, AWS, and Jupyter Notebooks, and improved OTA update systems. I've built ETL processes to streamline data ingestion from varied data sources, enhancing data availability for data scientists and ML engineers. I've collaborated with ML engineers to build scalable workflows and automated ETL pipelines using CI/CD tools like Jenkins, Ansible, and Airflow, with Git for version control.
Ideated and created mockups, UML diagrams, and lean business plans for the Internshala Student Portal. Formulated the technical process flow for functionalities and working of online training system.
I build AI systems end to end — LLM agents and multi-provider GenAI services on top of solid ML models, data pipelines, and infrastructure. The goal is always the same: systems that hold up in production, where the decisions matter.
I design and ship LLM-powered services end to end: agentic workflows that summarize and reason over real business data, multimodal analysis across text and images, PII redaction and content moderation, and MCP servers that let other AI agents call these capabilities as tools. I work across providers — Claude (Anthropic), Azure OpenAI, and Vertex AI — calling multiple models in real time with structured, machine-readable outputs.
I build predictive models that drive real product decisions — including multi-stage gradient-boosted cascades that refine their predictions as new signals arrive. Trained on distributed data at scale, and treated as products: feature pipelines, monitoring, and evaluation matter as much as the model itself.
Models are only as good as the data behind them. I design and maintain pipelines on Spark, Hadoop, Hive, and Kafka that turn massive, messy datasets into reliable features and signals — batch and real-time — so both ML models and LLM systems have reliable inputs.
I run AI workloads on cloud infrastructure using AWS, Docker, Kubernetes, and Terraform — deploying and scaling services with an emphasis on reliability, observability, and cost. Infrastructure as code by default.
I automate the path to production with Jenkins, Airflow, and Git-based workflows — including CI validation and security scanning for agent configurations, and tooling that generates LLM-readable documentation for codebases. Shipping AI safely should be repeatable, not heroic.
Project implemented as a part of Lyft's Prediction challenge on Kaggle
This is a ~1000 line distributed key value store, with support for replication, multiple machines, and multiple drives per machine. Optimized for values between 1MB and 1GB. Inspired by SeaweedFS, but simple. Should scale to billions of files and petabytes of data.
The goal was to develop a system for monitoring and predicting social unrest using natural language processing (NLP) techniques and transformer-based neural network models. The system was designed to analyze ACLED data in real-time and identify patterns and trends that could indicate potential unrest or conflict.
Python package for to understand the geographical context of social media activity
Led development of full-stack digital wallet app, enabling users to connect bank accounts, transactions, pay bills, and earn cashback