Gayathri Ravinuthala

I’m a Senior AI/ML Engineer with over 5 years of hands-on experience designing, building, and maintaining end-to-end data and machine learning pipelines across Financial Services, Aviation, Retail, and Healthcare. I focus on translating messy, multi-source data into secure, scalable systems that deliver measurable business outcomes.

I’ve developed real-time event-streaming solutions for fraud detection, using Apache Kafka and Spark Structured Streaming to enable detection within seconds. I also built multi-source ingestion patterns with Snowflake Streams & Tasks for CDC, then strengthened model quality through advanced feature engineering in PySpark.

On the modeling and ML lifecycle side, I manage experiments and production readiness with MLflow, including experiment tracking, model versioning, deployment, and monitoring. I build interpretability into production flows as well—integrating LLM-powered RAG pipelines to provide context-sensitive explanations for compliance teams.

I’m equally strong on data governance, quality, and compliance—implementing standards like HIPAA, HL7, and FHIR to ensure secure and standardized handling. Across projects, I’ve optimized distributed Spark workflows and queries to reduce end-to-end processing latency by ~35% and improved analytical cost efficiency by ~40%, while delivering reliable dashboards and inference endpoints via APIs and containerized services.