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Sai Chandu Machavarapu

@saichandumachavarapu

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MLOps engineer building production ML pipelines, model serving, and monitoring across AWS, Azure, and GCP.

United States

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What I'm looking for

I’m looking for an MLOps/AI platform role where I can own end-to-end ML pipelines, automate training-to-deployment, and strengthen model monitoring for reliability—while leveraging GenAI workflows like RAG, fine-tuning, and safe promotion.

I’m an MLOps Engineer and AI/ML practitioner focused on end-to-end machine learning: building ML pipelines, model serving infrastructure, and production monitoring systems. I automate model training, deployment, versioning, and retraining with MLflow, Kubeflow, Apache Airflow, Docker, and Kubernetes—then orchestrate reliable releases using CI/CD and infrastructure-as-code with GitHub Actions and Terraform.

I also bring strong GenAI/LLM capability—fine-tuning, RAG, and prompt engineering—backed by real deployment thinking like champion/challenger shadow deployments, feature engineering, and data drift detection. In projects, I’ve delivered measurable performance improvements (e.g., ensemble stacking lowering RMSE by 18%), achieved low-latency inference (sub-100ms), and built monitoring stacks with Prometheus, Grafana, AlertManager, and Evidently AI to drive safe promotion and retraining decisions.

Experience

Work history, roles, and key accomplishments

Machine Learning Intern

Codegnan

Nov 2023 - Apr 2024 (5 months)

Engineered an end-to-end house price prediction ML pipeline using Python, scikit-learn, and XGBoost, using MLflow to track 12+ experiment configurations and improving RMSE by 18% via ensemble stacking and hyperparameter tuning. Containerized a FastAPI inference service with Docker and achieved sub-100ms responses, while implementing automated CI/CD validation and data quality checks for 5,000+ rec

Python scikit learn XGBoost MLFlow Docker fastAPI GitHub Actions DVC Great Expectations Kubernetes

Java Full Stack Trainee

Wipro

May 2023 - Oct 2023 (5 months)

Developed backend REST APIs for an HR management application using Spring Boot, Hibernate/JPA, and MySQL with JWT authentication and role-based access control, integrating with a React frontend. Built CI/CD automation with Jenkins and GitHub Actions and contributed an HR module for employee records, attendance, and leave that was used by 50+ internal users during UAT.

Java Spring Boot JPA MySQL JWT REST APIs CI CD Jenkins Docker React

AI/ML Training (Virtual)

Amazon Web Services (AWS)

Mar 2022 - May 2022 (2 months)

Trained and deployed supervised ML models on AWS SageMaker using managed training jobs with spot instances to reduce training compute costs. Built ETL pipelines with AWS Lambda and S3, deployed auto-scaling SageMaker inference endpoints, and used CloudWatch alarms to monitor latency and error rates.

AWS Sagemaker AWS Lambda Amazon S3 Cloudwatch ETL CloudFormation SPOT Instances