Uduak Ituen
Machine Learning & MLOps Engineer
Canada-based Machine Learning Engineer with over 2 years of experience as a Software Engineer and over 3 years in Machine Learning Engineering. Specialized in the research and application of state-of-the-art machine learning solutions, from development to production.
Get in TouchWork Experience
Agriculture and Agri-food Canada
Machine Learning & Data Research Assistant (Contract)
Feb 2025 — June 2025- Collaborated with a cross-functional research team to translate business requirements into technical solutions, ensuring seamless integration between research and production environments
- Architected production-ready ML infrastructure for model serving using Docker, Apache Airflow, and MLflow within 5 months to accelerate ongoing research workflows
- Implemented automated CI/CD pipelines for ML model deployment, enabling rapid model iteration and consistent research workflow automation
- Initiated active learning methodology for continuous model improvement, automating uncertainty-based data selection to optimize and reduce the cost of wet lab experiments and enhance model accuracy
- Developed comprehensive documentation for data and deployment strategies, following MLOps and project managament standards. Enabling knowledge transfer among members of the team
The University of the Fraser Valley
Software Engineering Instructor (Contract)
August 2024 — December 2024- Delivered technical training to 94 students on modern development practices including Git version control, CI/CD pipelines, and Agile methodologies
- Designed hands-on curriculum featuring project-based assessments aligned with industry DevOps practices
- Mentored students on troubleshooting complex technical problems and communicating solutions effectively
- Guided students through full software development lifecycles, from requirement gathering to deployment
The University of Alberta
Graduate Research Assistant - Machine Learning
May 2022 — June 2024- Fine-tuned self-supervised ML models (VIME, TabNet) for telecom fraud detection, demonstrating measurable performance gains over classical supervised learning approaches
- Enhanced model accuracy by 2% through TabGAN implementation for synthetic data generation, optimizing performance against baseline supervised models
- Conducted literature reviews and comparative analysis of state-of-the-art fraud detection techniques, establishing benchmarks and evaluation metrics for model performance assessment
- Orchestrated large-scale model training using AWS SageMaker and Canada’s High-Performance Computing infrastructure for GPU-accelerated deep learning workflows
Fidelity Bank
Software Engineer
August 2019 — July 2021- Maintained critical product-level banking systems serving thousands of users, ensuring 99.9% uptime through proactive monitoring
- Automated cross-departmental processes eliminating manual workflows and improving operational efficiency
- Managed ETL operations for enterprise data warehouse, optimizing data pipelines and reducing processing errors by 25%
- Developed monitoring dashboards for real-time system performance tracking and KPI reporting
- Provided technical support for internal applications including hardware inventory systems and investigation portals
- Collaborated with full-stack development team on product-level system redesign projects, implementing enterprise-grade software engineering principles and improving user engagement by 30%
Featured Projects
About Me
I’m Uduak Ituen, a machine learning engineer with a strong foundation in mathematics, software engineering, and research. I specialize in transforming complex models into production-ready systems, from data pipelines to deployment. My journey began in financial software engineering, where I honed agile development and object-oriented design—skills I now apply to scalable ML infrastructure. At the University of Alberta, I deepened my expertise in machine learning research, working on projects in health and biotech that leveraged data-driven insights for real-world impact. My recent work spans large language models, generative AI, and self-supervised learning, with a focus on interpretability and precision. I thrive at the intersection of theory and application, building ML systems that don’t just work in the lab—they deliver in the real world.
