Sahil Bavishisahilbavishi.com, 2026

01About

London

Product, AI/ML and Data Science. I build the models, put them into production and work with clients on the product.

I build machine learning models and agentic pipelines at Really Good Culture, and before that I built LLM inference infrastructure at Cognext. I finished my MSc in Data Science at Edinburgh with distinction.

Outside work, I build. Each product starts with a story and someone I want it to work for, and I keep shaping it until it's something I'd want to use myself. If there's something new in AI/ML I want to learn, I just build with it.

02Education

Education

MSc Data Science at Edinburgh, with distinction, 2024 to 2025. Machine learning systems, computer vision and behaviour analytics.

B.Tech Data Science and Engineering at Manipal, 2020 to 2024, 8.84 out of 10.

03Projects

Projects

  • 01 Agent Studio One click to a working agentic system. You can see what it is doing. Try it
  • 02 GPU-Accelerated Top-K Retrieval and RAG Serving 11× the throughput. 0.91 recall at 1000, on 200K by 1M, under load. PyTorch
  • 03 Agentic Course Assistant Built at HackTheBurgh. Runner-up out of more than 100 teams. LLM Agents
  • 04 Image Segmentation with Deep Learning U-Net, autoencoders and CLIP, compared properly. 0.852. U-Net

The path keeps going. Explorer

04Writing

Writing

  • 01 Financial Market Prediction using Debating LLM Agents The MSc dissertation itself. The full method, the baselines and the numbers. 2025
  • 02 Commercial analytics at Mumbai International Airport Undergraduate thesis, written from inside the airport it is about. 2024

All writing

05Work

At Really Good Culture I fine-tune and build machine learning models and agentic pipelines, then ship them to production. I also work client side on our product delivery: scoping what a client needs, building it and staying with it after it goes live. Across the company I work on system design, deciding how the data, models and agents fit together, and I designed the agentic framework behind Breakthrough, our retail agent.

Before that, I worked at Cognext. I built the batching and queuing layer for LLM inference, reshaping how requests flow so the GPUs stay busy and the API bill stops tracking usage one to one. I also wired agentic frameworks into enterprise platforms and built the ingestion and deployment path on AWS Bedrock.

Before Cognext, I worked at Mumbai International Airport. I automated its commercial analytics, cutting analysis time by 95%, and learned the basics of commercial thinking.

  • StackPyTorch, TensorFlow, LangChain, LangGraph, AWS Bedrock and Lambda, FastAPI, Flask, SQL, Spark, Hadoop.
  • Away from the screenChess. FIDE classical 1563.

06Contact

Contact

bavishisahil@gmail.com LinkedInGitHub

The other way roundExplorer

Everything above, laid out as a place. Six rooms on a dirt crossroads, doors that open and a small green person who is me.

Enter the Explorer
Arrow keys to walk. Enter at a door. Touch controls on phones