AI Platform Engineer

Rithik Kumar

I build production Kubernetes platforms—and I train the deep learning systems they need to serve.

I've always known myself through the things I build.

01

About

I build the infrastructure that puts models into production.

“I've always known myself through the things I build.”

I've worked on both sides of the model–platform boundary: building and operating enterprise Kubernetes infrastructure, and training deep learning systems for robotics and computer vision.

That combination shapes how I approach AI infrastructure. I understand what a production platform must provide because I've built the models and pipelines it has to serve: reliable deployment, observability, security, and a clear path from experimentation to operation.

02

Selected Impact

60%

Lower deployment lead time

10K TPS

Transaction infrastructure delivered

50K+

Stereo images auto-annotated

03

Featured Work

A selection of systems, research, and infrastructure I've built.

Enterprise Kubernetes Platform technical visualization
Sanitized conceptual architecture
  1. 01Developer
  2. 02CI/CD
  3. 03Kubernetes Platform
  4. 04Application Workloads
  5. 05Observability / Security

01 · Platform Engineering / Kubernetes / Cloud Infrastructure

Enterprise Kubernetes Platform

Production platform infrastructure that makes application delivery more secure, observable, and repeatable.

60%

Lower deployment lead time

  • Kubernetes
  • Tanzu
  • AWS
  • Helm
  • Docker
  • cert-manager
View case study
Generative Motion Planning for Robotics technical visualization
Robotics planning pipeline
  1. 01Stereo Camera
  2. 02Perception
  3. 03Learned Model
  4. 04Candidate Trajectories
  5. 05Trajectory Optimization
  6. 06Robot Motion

02 · AI / Robotics / Motion Planning

Generative Motion Planning for Robotics

A perception-to-motion pipeline combining stereo calibration, learned trajectory generation, and constrained optimization.

End-to-end

Perception-to-motion research pipeline

  • Python
  • PyTorch
  • Drake
  • OAK-D Pro
  • Computer Vision
View case study
Stereo Perception & Semantic Segmentation technical visualization
Stereo perception pipeline
  1. 01Stereo Input
  2. 02YOLOv8 Segmentation
  3. 03CREStereo Disparity
  4. 04Dense Spatial Perception

03 · Computer Vision / ML Research

Stereo Perception & Semantic Segmentation

A stereo vision pipeline for segmentation, dataset automation, disparity refinement, and dense spatial perception.

50K

Stereo images auto-annotated

11 FPS

Dense spatial perception

  • Python
  • PyTorch
  • OpenCV
  • YOLOv8
  • CREStereo
View case study
04

Experience

Jul 2024 — Present

Austin, TX

Software Development Engineer

United Airlines

Enterprise platform engineering for production application delivery.

  • Designed and built an on-prem Kubernetes deployment platform on Tanzu, reducing deployment lead time by 60%.
  • Owned platform layers spanning cluster provisioning, networking, Helm releases, CI/CD, autoscaling, and workload scheduling.
  • Implemented namespace isolation, RBAC, controlled egress, automated certificates, and production observability.

Aug 2023 — May 2024

Boulder, CO

Research Assistant

Human Interaction and Robotics Lab

Robotics research spanning perception, machine learning, and motion planning.

  • Developed Python data analysis and machine learning pipelines for close-proximity human–robot interaction.
  • Built stereo perception, segmentation, and trajectory optimization workflows with PyTorch and Drake.

Jun 2021 — Jul 2022

Bangalore, India

Associate Software Development Engineer

Publicis Sapient

Backend and performance engineering for high-throughput systems.

  • Used Gatling and TDD workflows to improve REST microservice performance and test reliability.
  • Reduced HTTP cache usage by 30% through GraphQL query optimization.

Apr 2020 — Jun 2020

Bangalore, India

Software Development Intern

Publicis Sapient

Backend development for an internal project management platform.

  • Built and optimized REST APIs for performance and scalability.
05

Technical Depth

Platform & Infrastructure

  • Kubernetes
  • Tanzu
  • AWS
  • Docker
  • Helm
  • cert-manager
  • Venafi

ML & Perception

  • PyTorch
  • TensorFlow
  • OpenCV
  • YOLOv8
  • Drake
  • ROS

Observability & Streaming

  • Dynatrace
  • Datadog
  • Kafka

Languages

  • Python
  • Java
  • C++

Also worked with: React · Node.js · PostgreSQL · Redis · GraphQL · MongoDB · MySQL · DynamoDB

06

Education

Aug 2022 — May 2024

Boulder, CO

Master of Science in Computer Science

University of Colorado Boulder · GPA 3.96

Aug 2017 — May 2021

Warangal, India

Bachelor of Technology, Electrical and Electronics Engineering

National Institute of Technology Warangal · GPA 3.33

07

Contact

Let's build something that has to work in the real world.

For conversations about AI platforms, infrastructure, robotics, or production engineering.