Surajit Paul is a seasoned AI and Data Platform Engineer with 20+ years of experience in designing and building large-scale distributed data systems and advanced AI-driven applications. His expertise lies in developing LLM-powered solutions, including Agentic AI and Retrieval-Augmented Generation (RAG) frameworks, with a strong focus on production-ready systems that improve performance, reduce costs, and drive innovation. He has delivered measurable impact, including 3x improvement in customer engagement and revenue, 40% reduction in infrastructure costs, 10–15% increase in model prediction accuracy, and optimization of 3TB data processing from 27 days to 8 hours.
Currently serving as a Lead Platform Engineer at Stitch Fix, Surajit is responsible for architecting modern data platforms and supporting AI and machine learning infrastructure. Before this, he worked as an AI Architect at AT&T, where he designed and implemented generative AI and agentic AI solutions across enterprise-scale applications.
Surajit previously held a leadership role at Together Labs, where he led AI and data platform engineering initiatives and managed an MLOps team. His contributions included building distributed data pipelines, deploying Agentic RAG systems, achieving 3x growth in customer engagement and revenue, reducing infrastructure costs by 40%, and improving model prediction accuracy by 10–15%. Earlier in his career, he spent several years at Autodesk as a Principal Engineer and Solutions Architect, spearheading large-scale data lake initiatives and optimizing 3TB data processing workloads from 27 days to 8 hours, while building scalable cloud-based data streaming and microservices architectures.
He began his career with roles at Headstrong (Acquired by Genpact in 2011), IBM, Lennox International, and the University of Texas at Dallas, where he developed expertise in Hadoop ecosystems, recommendation systems, and enterprise application development, including contributing to systems that increased online revenue by 12%.
Surajit holds a Master of Science in Information Technology and Management from the University of Texas at Dallas and a Bachelor of Technology in Civil Engineering from the National Institute of Technology, India. He also holds multiple professional certifications, including AWS Certified Solutions Architect Associate, AWS Certified AI Practitioner, and Project Management Professional (PMP).
His technical skill set spans cloud platforms, distributed systems, machine learning frameworks, and modern AI tooling, including expertise in AWS, Trino, Spark, Druid, Flink, Kafka, Kubernetes, Databricks and emerging Agentic AI ecosystems such as LangChain, LangGraph, MCP Server and vector databases.