AI Engineering Manager

Job Purpose

The AI Engineering Manager is responsible for designing, building, integrating, and operationalizing Artificial Intelligence capabilities across the Bank. The role leads the technical implementation of AI platforms, AI services, Agentic AI solutions, Generative AI applications, and the NBB Decision Intelligence Platform (NDIP). The role holder will work closely with Enterprise Architecture, Data Engineering, Application Engineering, Cyber Security, Risk, Compliance, and business stakeholders to establish scalable, secure, and governed AI capabilities. The role will be responsible for AI engineering standards, AI platform development, AI solution integration, AI orchestration frameworks, Human-in-the-Loop controls, and AI operationalization.

Key Responsibilities

AI Engineering & Platform Development

• Lead the design and implementation of AI platforms and services across the Bank.
• Design and develop NDIP components including orchestration services, AI workflows, decision APIs, and Human-in-the-Loop capabilities.
• Establish AI engineering standards, reusable frameworks, and best practices.
• Design and develop secure APIs and microservices supporting AI-enabled business processes.
• Develop and maintain backend services using technologies such as Node.js and TypeScript, implementing modular services for decision APIs, case management, workflow orchestration, and audit logging.
• Support the implementation of workflow orchestration mechanisms to manage decision processes involving automated steps and human approvals.

Agentic AI & Intelligent Automation

• Design and implement Agentic AI solutions supporting banking use cases.
• Build and support the business to build AI agents capable of executing multi-step workflows and business processes.
• Evaluate and implement agent orchestration frameworks and emerging AI technologies.
• Establish Human-in-the-Loop controls, approval workflows, and governance mechanisms for autonomous and semi-autonomous AI solutions.

AI Integration & Solution Enablement

• Integrate AI services with enterprise platforms including Core Banking, Fraud, AML, Data Warehouse, Digital Channels, and other systems.
• Develop and maintain AI Gateway capabilities supporting model routing and orchestration.
• Implement Retrieval-Augmented Generation (RAG) and enterprise knowledge solutions.
• Support deployment and integration of Generative AI solutions across business functions.
• Integrate NDIP with enterprise AI systems including machine learning models, LLM-based services, and AI scoring engines.
• Assist in implementing mechanisms to capture AI outputs, confidence scores, and explainability data to ensure transparency in decision-making.
• Develop APIs and services for retrieving customer, transaction, and risk-related information required for decision-making.
• Support implementation of event-driven and API-based integrations with enterprise systems.

AI Infrastructure, MLOps & LLMOps

• Establish AI engineering environments and deployment pipelines.
• Implement model lifecycle management, monitoring, observability, and governance controls.
• Support AI platform scalability, resilience, and operational stability.
• Collaborate with infrastructure teams to support cloud-native AI workloads.

Workflow & Case Management

• Support the implementation of case lifecycle management including case creation, routing, escalation, review, and closure.
• Help ensure that decision workflows support human-in-the-loop review processes, escalation rules, and SLA monitoring.
• Contribute to implementing workflow processes supporting multi-step approvals and compliance requirements.

Platform Support & Maintenance

• Provide technical support for the NDIP platform, including troubleshooting issues, monitoring platform performance, and resolving defects.
• Assist in maintaining platform reliability, availability, and operational stability.
• Support system enhancements, upgrades, and performance improvements as the platform evolves.

Governance & Audit

• Assist in implementing mechanisms to maintain complete decision traceability, including decision inputs, rules triggered, AI outputs, user actions, and final outcomes.
• Support the implementation of audit logging and monitoring capabilities to meet regulatory and governance requirements.
• Work with risk and compliance teams to ensure appropriate AI and decision governance controls are embedded in the platform.
• Implement AI governance, traceability, and auditability controls.
• Implement monitoring and controls for AI outputs and decision processes.

Qualifications & Experience

• Bachelor’s degree in computer science, software engineering, information technology, or a related discipline is required.
• Master's degree in computer science, software engineering, information technology, or a related discipline is preferred.
• Additional certifications or training in software engineering, cloud technologies, or AI/ML technologies are preferred.
• Minimum 5-6 years of experience in software engineering, platform development, or backend application development with 3 years in the same or similar function.