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Rakesh Baddam

@rakeshbaddam1

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I build production AI systems, from RAG applications and LLM serving to optimized ML infrastructure.

United States

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What I'm looking for

I'm looking to build and operate production AI systems, especially LLM, RAG, MLOps, and data infrastructure solutions that deliver measurable value and work closely with technical and business stakeholders.

At Molina Healthcare, I built AI infrastructure and AI-driven applications from proof of concept through production. My Python, LangChain, and FastAPI RAG system reduced manual document search effort by about 70% and improved retrieval relevance by about 35%.

I optimize and deploy models using TensorRT, ONNX, quantization, CUDA, and vLLM, while troubleshooting GPU utilization and inference bottlenecks. I also built feature pipelines, feature stores, automated training and monitoring systems, and MLflow traceability for reproducible model lifecycles.

Previously at Meditab Software, I built scalable healthcare ETL/ELT pipelines with Python, SQL, and Apache Spark, processing millions of records while improving pipeline speed and reliability. I enjoy making AI and data systems dependable, explainable, and useful for both technical teams and business stakeholders.

Experience

Work history, roles, and key accomplishments

AI/ML Engineer

Molina Healthcare

Jan 2025 - May 2026 (1 year 4 months)

Drove end-to-end development of AI infrastructure and applications, including a RAG system that reduced manual document search effort by ~70% and improved retrieval relevance by ~35%. Optimized models with TensorRT, ONNX, and CUDA, and implemented production monitoring and observability.

Python TensorFlow PyTorch TensorRT Onnx CUDA VLLM RAG RLHF PPO MLFlow fastAPI AWS Azure GCP

Data Engineer

Meditab Software

Jul 2021 - Jul 2024 (3 years)

Designed scalable ETL/ELT pipelines using Python, SQL, and Apache Spark, processing millions of healthcare records and reducing pipeline execution time by ~35%. Built automated data-quality validation frameworks and used Databricks and Snowflake for distributed processing.