Jamal Johnson

Designed and deployed end-to-end Generative AI applications and scalable ML architectures on AWS, integrating LLMs, vector DBs (Chroma, Neo4j), and MLOps pipelines to enable real-time inference and CI/CD, improving model lifecycle automation and inference latency.

Architected RAG-based systems and real-time ML pipelines using Databricks, PySpark, Azure services and containerized deployments, reducing inference latency and enabling scalable production ML for recommendation and anomaly-detection systems.

Developed real-time IoT and edge ML solutions, built Spark/Databricks data pipelines and integrated OCR and computer vision workflows to improve anomaly detection and recommendation performance in production environments.

Built Python frameworks, orchestrated streaming and batch data pipelines on GCP (Dataflow, BigQuery, Cloud Functions) and automated deployments with Terraform and Jenkins to improve data processing efficiency and reduce query times.

Developed Java/J2EE web services and Spring-based applications, optimized SQL queries and implemented caching to improve application performance and reliability across enterprise projects.

Completed a Master of Science in Computer Science, focusing on advanced topics in computing and applied research.

Completed a Bachelor of Science in Computer Science with foundational training in software engineering and data systems.