Officer, Machine Learning & Artificial Intelligence Operations
Summary
MLOps/AIOps engineer responsible for deploying, monitoring, automating, and managing production AI/ML systems across the enterprise, including LLMs, RAG pipelines, and predictive models, using Azure, Kubernetes, Docker, MLflow, Databricks, and CI/CD.
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.
- 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.
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)