Pearl Bank is a Commercial Bank whose purpose is fostering prosperity for Ugandans. Our high impact goals are driving sustainable financial inclusion and stimulating entrepreneurship and service. The Bank is growing and giving opportunity for its competent and achievement driven employees as well as the public to fill up the role that has fallen vacant.

The role holder will be charged with establishing and leading Pearl Bank's central AI/ML capability, transforming the institution into an AI-driven leader in financial services. This role is responsible for architecting the AI-ready data foundation, researching, developing, and deploying production-grade machine learning models and AI agents that deliver tangible business value. The manager will spearhead a portfolio of strategic initiatives, leveraging a state-of-the-art tech stack centered on Python, TensorFlow, and Large Language Models (LLMs).

Responsibilities or duties

AI/ML Strategy & Roadmap:

• Define and execute the bank's AI/ML strategy, creating a prioritized roadmap of initiatives that align with key business objectives in risk, customer service, and operational efficiency.
• Establish the vision and lead the implementation of the AI-ready Data Warehouse, ensuring it provides a clean, reliable, and feature-rich foundation for model training and inference.
• Act as the bank's thought leader on AI/ML, educating stakeholders on capabilities, limitations, and ethical use of AI.

End-to-End Model Development & MLOps:

• Lead the end-to-end lifecycle of AI/ML solutions, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
• Architect, build, and train sophisticated ML models using Python and TensorFlow.
• Implement MLOps practices to automate the training, versioning, and deployment of models, ensuring reproducibility and scalability.

LLM Integration & Advanced AI Solutions:

• Strategically integrate Large Language Models (LLMs), both external (like OpenAI GPT, Claude) and internal, into the bank's solutions to power advanced natural language understanding and generation.
• Design and implement patterns for effective LLM usage, including Retrieval-Augmented Generation (RAG) for knowledge base querying, prompt engineering, and fine-tuning for domain-specific tasks.
• Oversee the development of intelligent agent frameworks that can orchestrate complex tasks across multiple systems.

Team Leadership & Cross-Functional Collaboration:

• Build, manage, and mentor a high-performing team of Data Scientists, ML Engineers, and AI Specialists.
• Collaborate closely with the Manager, Business Analysis to define AI project requirements and success metrics.
• Partner with the Manager, Full Stack Engineering to productize models as scalable APIs and with the Manager, Solution Integration to ensure seamless data flow from source systems.
• Work with the Manager, Solution Quality Assurance & DevOps to establish rigorous testing for models (e.g., fairness, bias, drift) and to implement CI/CD for ML pipelines.

Governance, Risk & Ethics:

• Establish a robust model governance framework, including documentation, version control, and performance monitoring for all production models.
• Proactively identify and mitigate risks associated with AI, including model bias, data privacy, security, and ethical implications.
• Ensure all AI solutions comply with regulatory standards and the bank's responsible AI principles.

Qualifications or requirements (e.g., education, skills)

Experience needed

• Bachelor’s degree in Computer Science, Software Engineering or a related course.
• Minimum of five (5) years' experience in active software engineering, with at least 1 year in a leadership role managing technical AI/ML engineering teams.
• Proven, hands-on experience in building and deploying AI & ML systems.
• Mandatory, deep expertise in Python and TensorFlow (or PyTorch).
• Direct experience with LLM integration, fine-tuning, and application development (e.g., using LangChain, LlamaIndex).
• Experience in the financial services industry, particularly with credit risk modeling, is a significant advantage.

BUSINESS BEHAVIOURS

• Passion: Committed to excellence, delivering outstanding results and making a positive impact on our customers and stakeholders.
• Teamwork: Collaborates, mutual respect, and diverse perspectives, to achieve shared success and deliver greater value to the Bank.
• Integrity: Uphold honesty, transparency, and accountability, ensuring ethical practices in every action.
• Innovation: Embrace creativity and forward-thinking, continually seeking new solutions to enhance customer experience and drive business growth.

• Define and execute the bank's AI/ML strategy, creating a prioritized roadmap of initiatives that align with key business objectives in risk, customer service, and operational efficiency.
• Establish the vision and lead the implementation of the AI-ready Data Warehouse, ensuring it provides a clean, reliable, and feature-rich foundation for model training and inference.
• Act as the bank's thought leader on AI/ML, educating stakeholders on capabilities, limitations, and ethical use of AI.
• Lead the end-to-end lifecycle of AI/ML solutions, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
• Architect, build, and train sophisticated ML models using Python and TensorFlow.
• Implement MLOps practices to automate the training, versioning, and deployment of models, ensuring reproducibility and scalability.
• Strategically integrate Large Language Models (LLMs), both external (like OpenAI GPT, Claude) and internal, into the bank's solutions to power advanced natural language understanding and generation.
• Design and implement patterns for effective LLM usage, including Retrieval-Augmented Generation (RAG) for knowledge base querying, prompt engineering, and fine-tuning for domain-specific tasks.
• Oversee the development of intelligent agent frameworks that can orchestrate complex tasks across multiple systems.
• Build, manage, and mentor a high-performing team of Data Scientists, ML Engineers, and AI Specialists.
• Collaborate closely with the Manager, Business Analysis to define AI project requirements and success metrics.
• Partner with the Manager, Full Stack Engineering to productize models as scalable APIs and with the Manager, Solution Integration to ensure seamless data flow from source systems.
• Work with the Manager, Solution Quality Assurance & DevOps to establish rigorous testing for models (e.g., fairness, bias, drift) and to implement CI/CD for ML pipelines.
• Establish a robust model governance framework, including documentation, version control, and performance monitoring for all production models.
• Proactively identify and mitigate risks associated with AI, including model bias, data privacy, security, and ethical implications.
• Ensure all AI solutions comply with regulatory standards and the bank's responsible AI principles.

• Python
• TensorFlow
• PyTorch
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Fine-tuning
• MLOps
• Data Exploration
• Feature Engineering
• Model Training
• Model Validation
• Model Deployment
• Model Monitoring
• Team Leadership
• Cross-Functional Collaboration
• Model Governance
• Risk Mitigation
• Ethical AI

• Bachelor’s degree in Computer Science, Software Engineering or a related course.
• Minimum of five (5) years' experience in active software engineering, with at least 1 year in a leadership role managing technical AI/ML engineering teams.
• Proven, hands-on experience in building and deploying AI & ML systems.
• Mandatory, deep expertise in Python and TensorFlow (or PyTorch).
• Direct experience with LLM integration, fine-tuning, and application development (e.g., using LangChain, LlamaIndex).
• Experience in the financial services industry, particularly with credit risk modeling, is a significant advantage.