Jatin Malhotra

Leading the charge in AI innovation at TrueBlue Inc., my focus is on developing scalable machine learning solutions that transform business operations. With a proven track record in AI/ML, I specialize in feature stores, data quality monitoring, and the creation of AI agents utilizing large language models (LLM) leveraging AWS Bedrock, AWS Sagemaker, ECS, RDS, DynamoDB, EMR, Microsoft Azure AI Studio

My hands-on approach has been instrumental in architecting low-latency, high-performance MLOps pipelines that deliver real-time model training and inference capabilities. At TrueBlue Inc., our team has successfully implemented cutting-edge technologies like agents, vector databases and prompt flows, enhancing the efficiency and intelligence of chatbots and other Generative AI-driven applications. This commitment to excellence is central to my professional ethos, as I continue to push the boundaries of what's possible in AI and machine learning.

Technical Advisor to early-stage AI startups; contributing to all aspects of the development process and incorporating standards and best practices into engineering solutions. Mentor software engineers within Next AI ventures; developing re-usable frameworks and reviewing design and code produced by other engineers. Provide expert-level advice to data scientists, data engineers, and operations for the delivery of high-quality solutions on projects.

Mentoring early stage AI startups at Chicago Accelerator

Led and managed Data Science practice by experimenting, designing, developing, and deploying statistical & machine learning models from scratch. Improved user click-through rate by 30% and implemented a user personalization feature with a recommendation engine using Locality Sensitive Hashing in Spark ML. Improved customer click-through rate (30%) by implementing user personalization including recommendation engine and re-ranking of search results using user behavioral, demographic, and real estate data sets (Alternative Least Square, Popularity, Amazon Personalize). Developed big data pipelines and machine learning models such as NLP Based Text Generator, Customer Segmentation and (Computer Vision) Single Image SuperResolution (GAN), Set up Big Data Infrastructure from the ground up on AWS EMR and Google DatProc with features for monitoring, maintenance, and enhancements of Spark Clusters on the platform. Productionized machine learning models as REST API on cloud platforms such as AWS, Google Cloud Platform (GCP).