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// about

Clive Lewis

I started out as a mechanical engineer in Mumbai. My undergraduate capstone was a coconut-harvesting robot programmed in Arduino C, and writing the code for it turned out to be the part I couldn’t put down. That pulled me toward software, and the first stop was enterprise work: a Software Engineering Team Lead role at Cognizant, maintaining a portfolio of Java integration interfaces for clients like British Airways under SWIFT banking compliance.

Enterprise systems taught me something I still lean on: when a failure keeps recurring, the fix you reach for first is usually a symptom patch. The real work is stepping back far enough to see the cause. That habit is why I came to New York for an MS in Computer Science at Pace, and why the classes that stuck hardest were the two Computer Vision electives nobody made me take.

I learn by building. Flutter, agentic AI architecture, the internals of a U-Net — in every case I got further by getting my hands on the problem than by reading about it first. I use AI coding tools daily to move fast through implementation details, which keeps my attention on architecture, judgment, and whether the thing actually works. And I try to be exact about what I’ve done and what I haven’t. A working prototype is not a production system, and saying so out loud is part of the job.

// experience

Where I’ve worked

  1. June 2025 – Present

    Software Development Intern

    Green2Gold Environmental Education Group · Remote

    I build and maintain a Python REST API backend paired with a Flutter mobile client, supporting content delivery and subscription workflows for the organization’s platform, on a PostgreSQL data layer with clean, reusable endpoints.

    I had never written mobile code before this role. I learned Flutter on the job and shipped production features within weeks, working largely independently and iterating on both backend architecture and mobile UI as requirements moved.

  2. January 2025 – May 2025

    Full Stack Web Development Associate

    Keymate.AI Inc. · New York, NY

    A 4-person engineering team reporting directly to the CTO. I owned a Chrome extension feature end to end — design, implementation, backend microservice integration, and production deployment — serving 1,500+ paying customers.

    I integrated the Google Gemini LLM API into the production application for real-time content summarization and owned the full loop: deploying the model, evaluating output quality against real usage, and iterating on direct paid-customer feedback. Alongside that I built CI/CD pipelines with Jest and Playwright so we could deploy continuously without holding our breath.

    Owning a feature for paying customers makes quality concrete. Users notice within hours when something breaks, and that changed how seriously I take testing and monitoring.

  3. January 2022 – April 2023

    Software Engineering Team Lead

    Cognizant Technology Services Ltd. · Mumbai, Maharashtra, India

    I built and maintained 190+ Java-based integration interfaces for enterprise clients including British Airways, holding 100% SWIFT banking compliance across the portfolio — enterprise financial data integration with real regulatory stakes.

    A recurring production issue kept failing cash remittance transactions under SWIFT cross-validation. My first instinct was to patch each failure as it surfaced, and that didn’t hold — they kept coming back. So I stepped back and traced it through the logs to the actual cause: mandatory system ID fields weren’t populating during cross-validation. I designed an automated Java validation routine that fixed it at the source, and that entire class of failure stopped. The lesson stuck: understand why something is failing before you fix what is failing.

    I also worked directly with business and IT stakeholders to integrate a chatbot and remittance systems with SAP S/4 HANA, improving data recovery by 15% — which meant translating what stakeholders actually cared about (timelines, risk, operational impact) into technical decisions rather than implementing a spec.

// research

Research

Published

Knee Osteoarthritis Detection Using Bone Distances

Clive Lewis, Tarun Ramapuram, Juan Shan. IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2025). New York City, New York — June 2025.

In plain terms: we measured the geometric distances between bones in knee MRI scans and trained a model to predict how severe a patient’s osteoarthritis is from those measurements. A MATLAB–PyTorch supervised deep learning pipeline over 160+ scans, reaching 76% accuracy and an AUC of 0.78.

This one started as curiosity. I took Dr. Juan Shan’s Computer Vision and Advanced Computer Vision electives because the subject interested me, not because they were required — the arc from classical image processing through to deep learning stayed with me. That interest turned into mentored research with Dr. Shan, and the research turned into a peer-reviewed publication.

To be precise about what this is: genuine applied deep learning on medical images driven by geometric measurement — model design, training, and evaluation. It is not object detection, tracking, or segmentation work, and I don’t describe it that way.

Unpublished · ongoing

SAM-Net

A two-stage approach to image segmentation. A U-Net produces an initial segmentation; a separate ML model then selects the most efficient points from it; those points go into SAM (Segment Anything Model) alongside the original image to produce a refined result.

I got properly stuck on this one. I could see that U-Net worked, but I couldn’t say why — what was actually happening inside the encoder-decoder. So I built visualizations of the intermediate flattened vector representations at each stage of the network, using AI tools to move quickly through implementation so my attention stayed on the concept. Once I could watch feature maps compress and expand through the network, the architecture stopped being an abstraction.

My test for whether I actually understand something complex isn’t whether I can describe it. It’s whether I can predict what an intermediate step will show before I look.

// education

Education

  • May 2025

    MS, Computer Science

    Pace University, Seidenberg School of Computer Science and Information Systems — New York, NY

    GPA 3.86 / 4.0

    Artificial Intelligence, Algorithms & Data Structures, Parallel Computing and Distributed Systems, plus Computer Vision and Advanced Computer Vision as electives.

  • June 2021

    BE, Mechanical Engineering

    Mumbai University, Don Bosco Institute of Technology — Mumbai, India

    Capstone: a coconut-harvesting robot, programmed in Arduino C. It worked, it was my first real taste of writing code that moves something in the physical world, and I have not claimed to be an embedded engineer since.

Get in touch

Open to Full-Stack, ML/AI, and Mobile engineering roles in the NYC metro area. The fastest way to reach me is [email protected].