AI ML QA SDET

Dr. Michael E. Scaman

Rowlett, TX Email: *******.*.******@*****.*** Phone: 845-***-**** CST LinkedIn: Michael Scaman

Summary

Versatile and innovative AI/ML Engineer and SDET with a Ph.D. in Electrical Engineering (specialized in statistical pattern recognition) and 25+ years of experience applying AI/ML and automation in high-impact domains. Proven leader in fraud detection, GenAI, computer vision, and scalable cloud-based systems. Strong proficiency in Python, Azure, Docker, and service orchestration. Actively trained in Azure, AWS and GCP to extend cloud-native AI deployment skills.

Many creative breakthrough accomplishments as well as perseverant day to say improvements and maintenance.

Certifications

GenAI & LLMs (NLP, RAG, LangChain, Fine-Tuning) – Simplilearn with Purdue University – 2024–2025

AI/ML/NLP/GenAI; Cloud AI: Azure (extensive use), AWS (in progress), GCP (in training); IAM and access control – UT Austin / Great Learning – 2024–2025

Python for AI/ML – Simplilearn – 2024

Big Data: Kafka, Hadoop, Snowflake, PySpark, Kafka – UT Austin – 2024

AI on Cloud; Cloud fundamentals – UT Austin – 2024

Cloud cert – Turbomomics 2016

Core Skills

AI/ML & GenAI: Prompt Engineering, RAG, LLMs, Fine-tuning, Computer Vision, Anomaly Detection

Languages: Python, Java, C#, SQL, HTML

Cloud & Containers: Azure, AWS, GCP, Docker, CI/CD

Frameworks: PyTorch, Scikit-learn, XGBoost, LangChain

Testing: Selenium, TestNG, JUnit, Performance, Load, Security, API/UI

Professional Experience

AI Instructor Simplilearn / Purdue University — Remote

Oct 2024 – Feb 2025

Instructed professionals in Python, GenAI tools, LLMs, RAG pipelines, and LangChain for applied AI use cases. Low code, no code and python coded GenAI across many architectural approaches.

Expert Witness Data Scientist fraud patterns — Remote

Dec 2023 – April 2024

Analyzed bar code data on fraudulent ID’s to determine if due diligence was done in a teen age fraud drunk driving case. Broke down the issues so jurors and lawyers could understand leading to an early settlement.

Broke down complex data so it could be easily understood driving to a good settlement.

Fraud Pattern Recognition Data Scientist Intellicheck, Inc. — Remote

May 2022 – Sep 2023

Built AI-driven fraud detection systems using Random Forests, anomaly models, and neural networks.

Deployed and validated scalable ML pipelines on Azure using Python, C#, SQL, and Docker. ETL, ELT, Alteryx, Looker, Excel.

Conducted stress testing of models under high-volume data inputs and edge scenarios.

Collaborated in Agile teams to test and deploy microservices in CI/CD pipelines.

Found scanner failure causing bad data allowing remediation strategies

Found data patterns where bad actors fabricated fake ID’s and shut them down

Found patterns in data allowing more sophisticated individual and ensample checking for fraud detection

Made presentations to stakeholders leveraging various visualizations and reported with KPIs

Wore various hats: SDET, Data Science, fraud pattern AI, software dev, DEVOPS

Digital Banking QA Architect Jack Henry — Allen, TX

Feb 2017 – May 2022

Led testing for microservices and APIs using Azure, Docker, and HTML-based services.

Built smart automation for UI, REST, and backend processes using Python, SQL and C#. Kafka, XSD, SOAP

Built significant intelligence into test pipelines, including shuffling and strategic reordering of test cases for complex systems.

This approach proved critical and often the only way to catch subtle race conditions and order-dependent issues.

Collaborated in Agile teams to test and deploy microservices in CI/CD pipelines.

Found subtle but serious order dependence issues

Found subtle but serious race conditions even with common Log4J fixes allowing the fixes to be fixed

Many checks in places for XSD contracts as well as the SOAP API data allowing a radical night to day quality transformation

Communicated stakeholders and onholre dev priorities with offshore

Was president and VP education for toastmasters for Texas Toast chapter at JHA

SDET Cloud Validation Turbonomic (IBM) — Valhalla, NY

Dec 2014 – Nov 2016

Python, Java, SQL, Rest

Developed AI-driven validation tools for cloud resource optimization.

Got cloud certification for virtual data center testing on cloud platforms.

Leveraged smart tooling for scalability, resource leak analysis, defect detection, and remediation.

Used Java profilers, Docker, and Robot Framework to detect and remediate resource leaks and scaling issues.

Collaborated in Agile teams to test and deploy microservices in CI/CD pipelines.

Found many show stopping serious resource leaks from the data

Earlier Roles VP test strategist Goldman Sachs, Cisco, Harmonic, General Dynamics, Advisory Eng IBM microelectronics and TJ Watson Mfg Research— Various Locations

1985 – 2014

Applied AI to clustering, vision inspection, and autonomous vehicle systems.

Used Matlab for visualizations

Led testing of streaming platforms, robotic image processing, and large-scale QA initiatives.

Machine vision for defect detection, human detection, sensor degradation detection.

HTML experience integrated into interface testing and diagnostics.

Delivered million-dollar impacts through AI-based test solutions and manufacturing automation.

Found previously unknown countermeasures to the systems and reported them for remediation

Found serious hardware flaws in the sterio infra red system allowing hardare redesign

Played key roles in billion-dollar product validation pipelines and decision-making tools.

Rapidly translated research and development findings into real-world fixes for critical manufacturing issues at IBM

In the early 80s found and remediate issues where data was intermittently repeated causing millions of dollars of scrap in IBM Endicott, Austin, Owego and Sindelfingen

Found issues in the computational tree allowing fixes

Found subtle issues with the statistical libraries for Goldman Sachs where most QA did not understand the mathematical issues

Found many critical shipment issues allowing IBM to ship and extra Billion dollars of high eng product

Education

Ph.D. Electrical Engineering (Statistical Pattern Recognition) – Polytechnic Institute of NYU

M.S. Computer Engineering – Syracuse University

B.S./M.S. Electrical Engineering – University of Illinois

Patents & Publications

24 patents in AI, pattern recognition, and semiconductor inspection

4 publications in vision and defect detection systems

Volunteer

TEALS Program Instructor – Python, Java, Cybersecurity (2018–Present)

Availability & Soft Skills

Preferred location: Dallas, TX Available for 3 days on-site as required.

Soft Skills: Versatile, perseverant, creative, and an excellent communicator, known for collaboration across technical and non-technical teams to drive results. Delivered millions in cost savings and quality improvements through expert systems, creative solutions, and applied machine learning—contributing to billion-dollar product validation impact in high-stakes environments.

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