Sai Akhil Rayapudi

I specialize in taking Machine Learning ideas from prototype to production by owning the data pipelines, training workflows, evaluation loops, and serving infrastructure that make models usable.Across marketing, healthcare, and finance, I’ve built systems where reliability and measurement mattered as much as accuracy.I approach ML problems with a strong data science lens starting from metrics, experimentation, and modeling choices before worrying about infrastructure.My work spans demand forecasting, recommendation systems, and predictive modeling on real business data, followed by building the ML and GenAI systems needed to operationalize those insights at scale.Technical Focus: Programming: Python, R, SQL, PySpark ML Engineering: Demand Forecasting, Recommendation Systems, NLP MLOps: Airflow, MLflow, DBT, Snowflake, Docker, Kubernetes Cloud: AWS (S3, Redshift, Glue, SageMaker, Bedrock) Generative AI: LangGraph, LLM fine-tuning (QLoRA/PEFT), vLLM, RAG, FAISSI’m particularly interested in teams working on scalable data pipelines, predictive modeling, ML/GenAI evaluation, and production observability.If you’re building ML systems that need to work reliably in the real world, I’d love to connect