Jewell Wright

Doctor of Engineering in AI and Machine Learning

About Me

I am an engineer, researcher, and educator specializing in distributed systems, data engineering, and machine learning infrastructure. My background spans DevOps, solution architecture, and technical enablement across enterprise AI and analytics environments.

I earned a Doctor of Engineering in Artificial Intelligence and Machine Learning, where my research focused on reinforcement learning for real-world engineering applications. I also serve as an adjunct Computer Science lecturer, teaching software development and programming with an emphasis on practical engineering principles.

Education

Doctor of Engineering, AI & Machine Learning, George Washington University, August 2024 - August 2026. Dissertation: Protecting the Learning Signal in Online ML Systems, ProQuest Publication No. 32791398.

Master of Science, Business Analytics, Quinnipiac University, January 2021 - August 2022

Bachelor of Science, Computer Science, Southern Connecticut State University, August 2016 - May 2019

Research

• My doctoral research focused on using reinforcement learning to filter incoming live data streams and preserve the integrity of the learning signal during online model training.
• I am also currently exploring dynamic workload coordination and distributed compute orchestration for scalable AI training and inference systems.

Skills

Python, SQL, Java, Spark, Databricks, Snowflake, AWS, Docker, Kubernetes, Jenkins, Git/GitHub, CI/CD, Infrastructure as Code (IaC), Distributed Systems, Machine Learning Infrastructure, Reinforcement Learning Systems, ETL/ELT Pipelines, Cloud-Native Architectures, Pipeline Orchestration, Real-Time Analytics, Infrastructure Automation, Data Engineering, AI Infrastructure, AI Assistive tools, Platform Engineering, Computer Hardware

Academic Experience

Adjunct Lecturer of Computer Science, Southern Connecticut State University, New Haven, CT

August 2023 - Current

• Designed and delivered technical curriculum for undergraduate Computer Science courses in Python programming, Object-Oriented Programming in Java, and end-to-end software engineering with an emphasis on applied systems development and industry-aligned engineering practices.
• Developed and maintained all instructional content independently, including programming labs, software engineering projects, technical assessments, deployment workflows, Git-based collaboration exercises, and cloud-integrated application development assignments delivered through Blackboard LMS.
• Instructed students in core software engineering concepts including object-oriented design, debugging, RESTful application development, version control, software architecture, database integration, and full software development lifecycle methodologies.
• Delivered hands-on technical instruction using industry-relevant development platforms and tooling including Replit, GitHub Classroom, JetBrains IDEs, Git/GitHub workflows, and Google Cloud Platform environments.
• Guided students through development of production-style applications using Python, Java, Flask, SQL, Git/GitHub, and cloud-native deployment practices within collaborative engineering-focused coursework.
• Mentored students on technical career preparation, software engineering workflows, cloud technologies, AI/ML career pathways, and modern development practices through one-on-one advising, technical project guidance, and code review support.
• Consistently received strong course feedback for technical clarity, real-world engineering integration, instructional quality, and the ability to translate complex computing concepts into applied development workflows.

Technical Advocate and Curriculum Developer, Snowflake, Remote

January 2025 - November 2025

• Served as a technical subject matter expert across Snowflake Cortex AI, Snowpark, supporting applied AI, data engineering, and real-time analytics initiatives within Academia.
• Developed hands-on labs and technical solutions using Snowpark, Cortex Search, vector embeddings, and Snowflake Container Services to support machine learning and AI application development workflows.
• Built scalable onboarding and certification enablement frameworks leveraging Snowflake Native Apps, role-based access automation, and reusable lab environments for large-scale AI enablement in Academia.
• Designed and delivered technical workshops focused on Snowpark ML, real-time data pipelines, streaming architectures, and cloud-native analytics workflows within the Snowflake ecosystem.
• Supported institutions and technical teams in implementing end-to-end data engineering and AI workflows using Snowflake platform technologies, with a focus on Cortex AI.

Professional Experience

Senior Solutions Engineer, Databricks, Remote

August 2023 - December 2024

• Architected scalable data and AI platform solutions for enterprise manufacturing customers using the Databricks Lakehouse Platform across batch, streaming, and machine learning workloads.
• Advised organizations on distributed data processing, ML pipeline orchestration, governance, and cloud-native infrastructure strategies for large-scale analytics environments.
• Designed technical solution frameworks integrating Spark, Delta Lake, streaming architectures, and ML workflows to support scalable enterprise AI adoption.
• Conducted deep technical engagements focused on platform optimization, workload scalability, and end-to-end data infrastructure modernization.
• Developed reusable technical assets, architectural patterns, and implementation accelerators to streamline distributed analytics and AI solution delivery in Spark.
• Maintained expertise across modern data infrastructure, machine learning systems, and distributed compute architectures to support complex enterprise deployments.
• Collaborated cross-functionally with Product and Engineering teams to translate emerging platform capabilities into production-oriented technical assets.

AI/ML Engineer, 3M, Remote

August 2019 - August 2023

• Engineered secure, HIPAA-compliant cloud-native data platforms and CI/CD infrastructure supporting large-scale research and life sciences workflows.
• Designed and optimized distributed data ingestion and transformation pipelines using AWS, Databricks, PySpark, and SQL for scalable analytical processing.
• Improved pipeline throughput by 30% through Jenkins parallelization strategies and infrastructure automation initiatives.
• Built automated frameworks for transferring and orchestrating research data between on-premises systems and cloud infrastructure environments.
• Developed platform tooling and Python-based utilities for binary and structured data translation, enabling scalable ingestion and transformation workflows within Databricks.
• Designed analytical processing jobs and query abstraction layers enabling researchers to interact with time-series infrastructure using SQL-based workflows.
• Built observability dashboards and monitoring solutions for real-time pipeline performance analysis and operational visibility.
• Mentored engineers on CI/CD workflows, Git-based development practices, and infrastructure automation standards to improve engineering efficiency and deployment reliability.