AI Solutions Engineer

Centenary Bank

About Centenary Bank

We are Uganda’s leading Commercial Microfinance Bank serving 3 million customers, with an asset base of UGX 6.3 trillion. Financial inclusion is key for us and we are reaching out through 81 branches, 209 ATMs, over 7,400 Agents, and several other digital channels including CenteMobile banking, CenteOnline banking, CenteVisa debit & prepaid cards, and Mastercard Platinum debit card.

Job Description

Centenary Bank seeks to recruit a suitably qualified, experienced and competent individual to fill the position below. This is an exciting opportunity for a highly motivated and result-driven professional.

The AI Solutions Engineer is responsible for designing, governing, and deploying enterprise-grade AI/ML solutions that support high-impact business use cases while ensuring compliance with regulatory, data privacy, and security standards. The role bridges advanced data science, engineering, and enterprise systems integration, enabling scalable and resilient AI adoption across the organization.

Responsibilities

Design and implement secure, scalable AI solutions aligned with the Bank’s enterprise architecture, technology standards, and target-state platforms.
Develop, train, deploy, and maintain AI/ML models and automated pipelines for production use, ensuring robustness and performance.
Establish and manage end-to-end model lifecycle processes including CI/CD, versioning, monitoring, retraining, and continuous improvement.
Build and manage APIs and integration services to embed AI capabilities into core banking systems, digital channels, and downstream applications.
Engage business and product stakeholders to translate banking use cases into practical, scalable AI solutions, support PoCs and pilot initiatives.
Ensure AI solutions comply with information security, data privacy, model risk management, and regulatory requirements.
Prepare technical documentation, conduct knowledge transfer, and support operational teams for smooth handover and ongoing support.

Requirements

Bachelor’s degree in computer science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related quantitative field.
Advanced certifications in Artificial Intelligence or Machine Learning, cloud-based AI/ML platforms (such as AWS, Azure, or Google Cloud), and MLOps or DevOps for model deployment and lifecycle management.
Certifications in data engineering, AI governance, responsible AI, information security, or data privacy are highly advantageous, along with enterprise or solution architecture and Agile delivery certifications to support large-scale, regulated banking environments.
At least five (5) years of progressive experience in software development and solution delivery within enterprise environments with a minimum of two (2) years’ experience in a role related to the design, development, and deployment of AI/ML, data-driven, or enterprise software solutions within a large financial institution or a reputable organization.
At least one (1) year of hands-on experience supporting data pipelines, APIs, or microservices integrated into enterprise or digital platforms.
Experience in translating business or functional requirements into technical solutions, including development, testing, and deployment in production environments.
Working knowledge of AI/ML model lifecycle activities such as training, validation, deployment, monitoring, or optimization.
Basic to intermediate understanding of information security and data protection concepts (e.g., encryption, access control, secure data handling).
Familiarity with RESTful APIs, Linux-based environments, software deployment, and scripting (e.g., Bash or Python) is desirable.
Ethics and Integrity
Teamwork and Cooperation
Productivity
Effective Communication
Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
Experience with MLOps tools and practices (CI/CD, model monitoring, versioning)
Knowledge of data engineering, APIs, microservices, and cloud platforms (AWS, Azure, or GCP)
Understanding of explainable AI, model risk management, and AI governance.
Strong analytical and problem-solving skills
Ability to communicate complex AI concepts to technical and non-technical stakeholders
Experience mentoring or leading technical teams