Krishna Kumar

I build data platforms that save companies millions, and now I'm making them intelligent with AI. Over 10+ years, I've architected and scaled distributed data systems end-to-end: real-time streaming pipelines, cloud-native platform infrastructure, and ML-ready data architectures on AWS and Kubernetes. I have lead Staff level platform-wide initiatives that eliminated over $1M in vendor costs and reduced platform spend by ~45%. Today, my work sits at the intersection of data engineering and AI/ML — where I believe the highest-impact opportunities live in 2026: → Building AI-native data pipelines: integrating LLMs, feature stores, and vector databases into production data platforms → Designing RAG architectures and GenAI workflows that turn unstructured data into automated, intelligent insights → Scaling MLOps infrastructure: model deployment, monitoring, CI/CD for ML, and real-time feature engineering on Kubernetes → Delivering end-to-end data solutions with Apache Flink, Spark, Kafka, and modern data warehousing (Redshift, Databricks, Snowflake) I think the future of data engineering is AI-native — where pipelines don't just move data, they understand it. Core stack: Python, SQL, Apache Flink, Spark, Kafka, Kubernetes, Docker, Terraform, AWS (Glue, Redshift, SageMaker, Bedrock), LangChain, PyTorch, MLflow, Vector DBs