Harshitha Vemula

About Harshitha Vemula

I’m a Python and AI/ML engineer who enjoys building systems that actually work in production — not just in notebooks.Over the last 4+ years, I’ve worked across healthcare, fintech, and enterprise platforms, designing scalable APIs, ML pipelines, and cloud-native systems on AWS and Azure. My work spans from data ingestion and model training to deployment, monitoring, and real-world optimization.Recently, I’ve been working extensively on Generative AI and MLOps, including GPT-based solutions, RAG pipelines, AWS Bedrock, SageMaker, and containerized ML deployments using Docker and Kubernetes. I’ve also built real-time data pipelines using Kafka, Spark, and Databricks to support analytics and AI workloads at scale.I enjoy working in fast-paced environments where engineering decisions directly influence product outcomes. I value clean architecture, well-documented systems, and close collaboration with product, data, and DevOps teams.What people usually come to me for:Designing production-ready ML pipelines (not just experiments)Python backends using Django, FastAPI, and FlaskCloud-native architectures on AWS & AzureMLOps, CI/CD, Docker, Kubernetes, and TerraformTurning complex data into systems teams can actually rely onI’m currently open to conversations around AI/ML Engineer, Senior Python Engineer, Data Engineer, and Applied AI roles, especially where ownership, scale, and real-world impact matter.Feel free to reach out — happy to connect and exchange ideas.

Experience

1. Ai Ml Engineer

   UnitedHealth Group

   Jul 2025 — Present · Chicago, IL, US

   Designed and deployed production-grade ML pipelines for customer churn prediction and sentiment analysis using large-scale healthcare datasets.Built and maintained MLOps workflows covering data validation, model training, evaluation, deployment, and monitoring.Developed REST APIs using Django REST Framework to serve ML predictions to internal applications.Implemented data pipelines with Pandas, Spark SQL, and cloud storage to ensure high-quality, reliable model inputs.Containerized ML services with Docker and collaborated with DevOps teams to deploy on cloud infrastructure.Performed A/B testing and statistical analysis to measure model impact and guide product decisions.Authored model documentation, data dictionaries, and model cards to support governance and explainability.

Education

• Gopalan College of Engineering and Management (GCEM), Bengaluru

  Bachelor of Engineering - BE, Computer Science

• University of Central Missouri

  Masters, Computer Science

• Narayana Institute

  10th, Science

• Narayana Institute

  12th, PCMC