Officer, Machine Learning & Artificial Intelligence Operations at Stanbic IBTC

Job Description

• The Machine Learning / AI Operations Engineer is responsible for ensuring the seamless deployment, monitoring, automation, and lifecycle management of AI/ML systems across the enterprise.
• The role ensures robust MLOps and AIOps capabilities, supports Data Scientists in operationalizing models, manages production workloads—including LLMs, RAG pipelines, and predictive models—and ensures compliance with governance and regulatory requirements.
• Support execution of the bank’s Data Science & AI strategy through reliable ML/AI operationalization.
• Design, build, and maintain end-to-end MLOps/AIOps pipelines for scalable deployment and monitoring.
• Work with IT, Data Engineering, and Architecture to align ML/AI infrastructure with enterprise standards.
• Manage CI/CD pipelines for model deployment, automated testing, and infrastructure-as-code.
• Monitor model performance, stability, latency, and data quality across production systems.
• Ensure all deployed AI/ML models comply with Model Risk Management guidelines and regulatory expectations.
• Evaluate and integrate emerging MLOps/AIOps tools and capabilities.
• Lead PoCs and pilot initiatives focused on scalable AI automation.

Qualifications

• Bachelor’s Degree in Computer Science, Engineering, Data Science, or related fields.
• Master’s Degree or certifications in Azure ML, AWS ML, Kubernetes, MLOps, DevOps, or Cloud Engineering are an advantage.

Experience:

• 4–6+ years in Machine Learning Engineering, MLOps, AIOps, or related roles.
• Experience supporting Data Scientists with model operationalization and deployment.
• Strong background managing production AI systems, including LLMs, RAG pipelines, and predictive models.
• Experience with Azure, Kubernetes, Docker, MLflow, Databricks, CI/CD, and model monitoring tools.

Additional Information:
Behavioural Competencies:

• Adopting Practical Approaches
• Articulating Information
• Challenging Ideas
• Developing Expertise
• Documenting Facts
• Embracing Change
• Interpreting Data
• Managing Tasks.

Technical Competencies:

• Big Data Frameworks and Tools
• Data Engineering
• Data Integrity
• IT Knowledge
• Stakeholder Management (IT).