Open to full-time roles in the US

Sarabesh Ravindranath

ML Infrastructure & MLOps Engineer · Bloomington, Indiana

I build reliable systems around machine learning: CI/CD, dataset and model versioning, training and batch-inference pipelines, and the observability to trust them in production.

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Sarabesh Ravindranath
4+ yrs
industry experience, plus ~2 yrs of applied ML research
40%
faster ML iteration with a DVC + GitLab CI pipeline
20→75%
automated test coverage on VMware Tanzu releases
5
certifications: AWS, CKA, CKS, Terraform, NVIDIA

I’m an ML infrastructure and MLOps engineer with 4+ years of industry experience across DevOps, cloud infrastructure, and Kubernetes platforms, plus ~2 years of applied ML research and internship work in computer vision and production ML workflows. I like taking models beyond notebooks: repeatable training pipelines, automated delivery, and production behavior you can measure.

I’m a research assistant at Indiana University’s School of Optometry, building reproducible 3D terrain-reconstruction workflows and training camera-orientation models on IU supercomputers. Before that, I worked on ML training and observability at Musco Lighting and shipped multi-cloud platform automation at VMware and Presidio.

I hold an MS in Data Science from Indiana University Bloomington (GPA 3.90). I’m interviewing for AI/ML, MLOps, ML platform, and LLM/RAG roles in the United States and am open to relocating.

Work authorization: F-1 STEM OPT with EAD valid through June 2028. H-1B sponsorship will be required thereafter for continued work authorization or change of status.

Experience

Research Assistant · Indiana University School of Optometry Jan 2025 — present
Bloomington, IN
  • Collaborate with faculty on the Walking Project, building reproducible 3D terrain-reconstruction and complexity-modeling workflows from data collected across 10+ trails and 8 subjects using RealityCapture.
  • Reconstructed 10+ scenes from binocular and monocular imagery, creating terrain-analysis assets for scene-understanding and surface-complexity experiments.
  • Plan field data collection and evaluate multimodal capture tooling including Meta Aria eye-tracking glasses, drone and overhead cameras, GPS trackers, and Shadow IMU body suits.
  • Train and evaluate single-image camera-orientation models on the Nymeria dataset using IU supercomputing workflows, SLURM job orchestration, environment modules, and distributed training as needed.
Emerging Tech Intern, AI Specialist · Musco Lighting May 2024 — Aug 2024
Des Moines, IA
  • Developed a modular MLOps pipeline with DVC and GitLab CI for dataset versioning, validation, retraining, and model promotion, improving iteration speed by 40%.
  • Implemented YOLOv5 training and batch-inference workflows integrated with MLflow for experiment tracking, artifact management, model comparison, and performance visibility.
  • Designed event-driven dataset synchronization between NFS and S3, reducing data latency by 80% and improving availability for downstream ML workflows.
  • Enhanced production ML observability with MLflow dashboards, drift signals, and data-change alerts to strengthen model lifecycle governance.
DevOps Engineer, MTS II · VMware Feb 2022 — Jun 2023
Bangalore, India
  • Contributed to the VMware Tanzu Application Platform (TAP) release team, automating multi-cloud installation and validation pipelines across EKS, AKS, and TKGS.
  • Designed and implemented Go-based end-to-end validation suites, increasing automated test coverage from 20% to 75% across platform-release workflows.
  • Integrated CI/CD workflows into Helm-based Kubernetes infrastructure, improving release reliability and reducing manual validation overhead.
  • Partnered with platform, QA, and release teams to optimize Kubernetes deployments, version-control integration, automated environment validation, and release readiness.
Cloud Engineer · Presidio May 2019 — Jan 2022
Chennai, India
  • Architected and deployed enterprise CI/CD pipelines using Jenkins and GitHub Actions, implementing infrastructure as code with Terraform and CloudFormation.
  • Built scalable AWS infrastructure for clients including ViacomCBS, StatsPerform, and WEX, supporting cloud-native deployment, cost optimization, and operational reliability.
  • Led the migration of Jenkins workers to Kubernetes pods, improving resource efficiency and reducing build times by 35%.
  • Developed internal DevOps libraries integrating Slack and Sumo Logic APIs for real-time alerting and monitoring, reducing mean time to recovery (MTTR).

Projects

Pokemon-AI-runner2026

An LLM discovers the controls of Pokémon Red and designs the reward function; a ~0.6M-parameter CNN trained with PPO then learns to play across 12 parallel headless Game Boys on a laptop GPU.

  • PPO
  • PyTorch
  • Stable-Baselines3
  • PyBoy
  • LLM agents
Banking computer-use agent2026

An LLM discovers a task once in a legacy back-office app with no API; the run compiles into a typed, versioned capability that replays deterministically without an LLM, with human takeover and 101 tests.

  • Computer use
  • Playwright
  • Claude API
  • Python
HybridRAG2024

Hybrid retrieval for RAG that combines vector search with a Neo4j knowledge graph, benchmarking GraphRAG against vector RAG on latency, contextual precision, and infrastructure trade-offs.

  • Neo4j
  • LangChain
  • Ollama
  • RAG
★ 54
PuppyDB2025

A vector database built from scratch: mmap’d flat files for vectors, LMDB for metadata, and a custom HNSW index for approximate nearest-neighbor search. Published on PyPI as pup-py-db.

  • HNSW
  • LMDB
  • Python
  • Vector search
★ 4
BERT sentiment MLOps2024

End-to-end model lifecycle: BERT fine-tuned on Sentiment140, served with FastAPI on Kubernetes, with batch prediction, drift detection, Slack alerts, and CronJob-driven retraining.

  • FastAPI
  • Kubernetes
  • Docker
  • Hugging Face
Neural-Recon2024

Reconstructs natural scenes from fMRI brain activity by mapping signals from the Natural Scenes Dataset into latent-diffusion and CLIP-aligned representations.

  • Stable Diffusion
  • CLIP
  • PyTorch
  • fMRI
More projects
GridWorld-PPOPPO path planning in a custom grid world with dynamic obstacles, reward shaping, and imitation-learning experiments.
mono-cam-DeepVODeepVO reimplementation: FlowNet-based CNN + LSTM predicting 6-DoF camera motion on KITTI odometry.
Retrieval-RerankerDense retrieval in Qdrant with cross-encoder reranking on BEIR MS MARCO, evaluated with nDCG, MRR, and recall.
reddit-recsysMultimodal Reddit recommendations: CLIP embeddings in Qdrant, orchestrated with Airflow on Kubernetes.
FinetuningBaseline guide for LLM post-training (SFT and RLHF) and evaluation on standard datasets.
exploring-transformersTransformer internals from scratch: BPE tokenizer, rotary embeddings, and GPT-2/Llama-style attention.
exploring-diffusionNotebook series exploring diffusion models for image generation.
SkinNERNamed-entity recognition for skin diseases, comparing spaCy NER with fine-tuned BERT.
Algae-ClassificationImage classification for algae identification with fine-tuned vision transformers in TensorFlow.

All repositories on GitHub ↗

Skills

ML / MLOps platforms
  • DVC
  • MLflow
  • SageMaker
  • Prefect
  • Airflow
  • Model registry
  • Experiment tracking
  • Dataset versioning
  • Batch inference
ML / AI systems & libraries
  • PyTorch
  • TensorFlow
  • scikit-learn
  • Hugging Face
  • LangChain
  • Open3D
  • TensorRT
  • Computer vision
  • RAG
  • Vector search
  • NLP
  • Reinforcement learning
  • Drift detection
Cloud & infrastructure
  • AWS Lambda
  • ECS
  • EKS
  • RDS
  • S3
  • Step Functions
  • IAM
  • VPC
  • Kubernetes
  • Docker
  • VMware Tanzu
  • NFS
  • SLURM
DevOps, CI/CD & IaC
  • Terraform
  • CloudFormation
  • Helm
  • Jenkins
  • Concourse
  • GitHub Actions
  • GitLab CI/CD
  • Argo CD
Languages, frameworks & data
  • Python
  • Go
  • C++
  • Java
  • Groovy
  • Flask
  • FastAPI
  • Spring Boot
  • MySQL
  • DynamoDB
  • Neo4j
  • Qdrant
  • LMDB

Writing

1.58 BitNet ↗

How BitNet-style quantization reduces memory requirements while preserving strong LLM performance.

All posts on Medium ↗

Education & credentials

Indiana University Bloomington2023 — 2025
Master of Science in Data Science · Luddy School of Informatics, Computing, and Engineering · GPA 3.90 / 4.00
Coursework: Applied Machine Learning, Elements of AI, Exploratory Data Analysis, Computer Vision, Applying ML Techniques in NLP, Deep Learning Systems, Autonomous Robotics
Velammal Engineering College, Anna University2015 — 2019
Bachelor of Engineering in Computer Science and Engineering · Chennai, India · GPA 8.39 / 10.00
AWS Certified Solutions Architect — AssociateAWS
Certified Kubernetes Administrator (CKA)CNCF
Certified Kubernetes Security Specialist (CKS)CNCF
Terraform AssociateHashiCorp
NVIDIA-Certified Associate: AI Infrastructure and OperationsNVIDIA

Connect

I’m open to full-time AI/ML, MLOps, and ML platform roles in the United States and available to interview.

GitHub LinkedIn Email Résumé

sarabeshnr@gmail.com · +1 930 333 4115