About

Sudhanva Narayana.

Senior ML Engineer at Montai Therapeutics in Boston, based in the SF Bay Area, working remotely. I build production ML infrastructure on Kubernetes.

At Montai I own end-to-end ML infrastructure: batch prediction pipelines processing 1B+ rows, multi-GPU build systems, auto-scaling Kubernetes clusters with Ray and Flyte, and the observability + cost-control that keeps it all honest in production.

Before Montai, I interned at Autodesk on a real-time event pipeline serving a transformer for next-click prediction, and earlier built multi-regional ML infrastructure for geo-spatial analytics at Pixxel, India's leading private space-tech company. Earlier still: ML at Initiable Intelligence, and software engineering at DCT Academy.

This site is where I write up the parts of the work worth writing up, long-form notes on production ML, infrastructure, vector databases, Kubernetes (managed and bare-metal), and the engineering decisions behind all of it.

Experience

  1. Jul 2023, present

    Senior ML Engineer Montai Therapeutics

    Boston, MA · Remote from SF Bay Area

    • Built batch prediction pipelines processing 1B+ rows across TensorFlow models in under 3 hours, accelerating drug-candidate evaluation.
    • Owned end-to-end ML infrastructure: auto-scaling Kubernetes with Ray and Flyte, observability (Prometheus, Grafana, alerting), $50k+/yr cloud-cost reduction.
    • Architected a multi-GPU build system that halved model deployment time; CI/CD + automated tuning save ~10 hours/week.
    • Shipped internal apps enabling cross-functional teams to run models on-demand with experiment tracking and model versioning.

  2. May 2022. Jul 2022

    Machine Learning Engineer Intern Autodesk

    San Francisco, CA

    • Deployed a high-throughput transformer on a real-time event pipeline, analysing 1M+ daily user interactions for next-click prediction.

  3. Jun 2020. May 2021

    Machine Learning Engineer Pixxel

    Bengaluru, India

    • Built multi-regional ML infrastructure for geo-spatial analytics, 75% inference-efficiency gain, 50% map-render latency reduction.
    • ETL + ML pipelines processing 1TB+/day; pipeline failures from schema issues cut by 30%.
    • Architected a GPU queue with automatic A/B testing for regional model selection, ~$50k/yr saved.

  4. May 2019. May 2020

    Machine Learning Engineer Initiable Intelligence

    Bengaluru, India

    • Led a team of 4 to build an NLP-powered chatbot API enabling 5-minute integration for static sites; adopted by multiple startups.

  5. Jan 2017. May 2019

    Software Engineer DCT Academy

    Bengaluru, India

    • Built a task recommendation engine with automated assignment matching, 60% student engagement lift.
    • Built a classroom-management tool with auto-grading and student clustering, ~2 hours/day saved for teaching staff.

Education

  1. Sep 2021. May 2023

    Master of Science, Artificial Intelligence

    Northeastern University Boston, MA

  2. Jun 2013. Nov 2016

    Bachelor of Science, Computer Science

    PES University Bengaluru, India