I'm a ML Engineer with 4+ years of experience building and scaling solutions across machine learning, optimization, and
Generative AI. With a Master’s in Data Science from Indiana University Bloomington (GPA: 4.0), I’ve applied my skills to
solve complex real-world problems at scale.
At Toyota, I led high-impact projects that generated multi-million dollar value across forecasting, optimization, and AI
automation. I designed and deployed a $150M+ Accessory Recommendation System and architected an Annual Planning
Optimizer using constrained linear programming (GurobiPy) improving manufacturing throughput and streamlining national
vehicle build planning. I’ve shipped full-stack ML systems using SageMaker, Airflow, Redshift, PySpark, and
containerized APIs on AWS, Databricks and Azure, ensuring production-grade performance and scalability.
Lately, I’ve been focused on building agentic GenAI experiences using LangChain, LangGraph, OpenAgents, and AWS Bedrock,
including "AskToyota" an internal Q&A assistant powered by LLaMA2 and FAISS that improved access to forecasting
documentation across business units. And right here on this site, you'll find “Ask About Prateesh”, a chatbot that
answers questions about my background, my projects, and even my dating life. It learns from feedback, stores unknown
questions, and improves over time. Go ahead, test it out!
If you're hiring or collaborating on ML, optimization, or GenAI initiatives, feel free to reach out the chatbot knows
where to find me 😉
👉 Or better yet,
Download my resume here or
Email me
Built and deployed a Generative AI chatbot hosted on this website
to answer detailed questions about Prateesh’s background — a
RAG-based system tailored for recruiters and hiring managers. Used
OpenAI’s GPT‑3.5‑turbo with custom prompt engineering, LangChain
for orchestration, and FAISS for vector search over ada text
embeddings. Stores unanswered questions for continoues
improvement. Deployed serverlessly via Cloudflare Workers with
CI/CD, logging, and auto-monitoring of unseen queries.
To Provide Cognitive search capability to search against database
like FDA and EMA (European medical agency) via a natural language
question and return relevant results in order to help with
accelerating regulatory submissions for Eli Lilly. Performed
abstraction based Natural language generation methods like T5
Transformer, GPT-2 Algorithm and BART Transformer
PixelGram is a distributed photo-sharing app built with a
microservices architecture using REST APIs and RabbitMQ for
asynchronous communication. It supports features like image
uploads, user authentication, and activity feeds. The system is
containerized with Docker and deployed via Jenkins pipelines on an
OpenShift cluster for scalability and modular development.
In Banking and finance sector, the term Blockchain is frequently
heard. Each block contains a cryptographic hash of the previous
block, a timestamp, and transaction data. This project is an
effort to give bank the ability to access a single source of
information and also allows them to track all documentation and
validate ownership of assets digitally, as an unalterable ledger
in real time.
