Engineer - MLOps

We are seeking an MLOps Engineer to streamline the transition of machine learning models from development to production. This role is critical to ensuring our model lifecycles-spanning Credit Risk, growth models, and pattern mining- are automated, monitored, and scalable within our Al ecosystem.

Key Responsibilities:

• Model Deployment & Serving: Deploy and manage production-ready ML models as scalable APIs or batch inference services.
• ML Pipeline Automation: Implement automated CI/CD pipelines specifically for ML model versioning and deployment.
• Monitoring & Observability: Set up systems to track model performance & governance, data drift, and prediction latency in real-time to ensure financial model integrity.
• Collaborate with Al Teams: Work closely with Data Scientists to refactor experimental code into modular, production-grade scripts.
• Maintain Engineering Standards: Ensure strict model governance, auditability, and best practices in model registry and version control.
• Agentic Al Infrastructure Support: Provide foundational infrastructure support for experimental Agentic Al workflows and LLM deployments.

Qualifications:

• Bachelor's degree in Computer Science, IT, or a related discipline.
• 2+ years in a hands-on MLOps, ML Engineering, or DevOps role.
• Previous roles: MLOps Engineer, Associate ML Engineer, or DevOps Engineer.
• Experience or strong interest in the finance domain is highly desirable.

Technical Skills:

• MLOps Tools :MLflow, Kubeflow, DVC, or Vertex Al.
• Model Serving :Bento ML, TFServing, or TorchServe.
• Programming :Python (Strong proficiency).
• Cloud Platforms :GCP.
• DevOps/Infra :Docker, Kubernetes, Terraform, and GitHub Actions

Soft Skills & Leadership:

• Ability to fit into the Combank organization & IT culture.
• Excellent attention to detail and analytical skills, specifically regarding model lineage and performance monitoring.