Design, build, and improve MLOps platform components that support the full model lifecycle (development à validation à deployment à monitoring).
Create reusable templates and standardized pipelines to reduce time-to-production and improve consistency across teams.
Implement robust deployment patterns for credit risk models (primarily batch; other patterns as required).
Build & maintain CI/CD pipelines using Jenkins and GitHub, with appropriate quality gates and traceability.
Automate environment configuration and repeatability using Ansible.
Implement model and pipeline monitoring covering operational health, data quality signals, and model performance/drift indicators.
Establish dashboards, alerting, and runbooks; partner with stakeholders to ensure alerts are actionable and aligned to business impact.
Drive continuous improvement through post-release reviews and reliability enhancements (no on-call requirement).
Work closely with credit risk modellers to productionise models built with tools such as TensorFlow, MLFlow, and similar.
Translate modelling needs into scalable engineering solutions, balancing pace with control expectations.
Mentor junior team members (nice-to-have) and contribute to shared engineering standards and documentation.

5+ years’ experience across MLOps/DevOps/Platform Engineering, with a track record of delivering production-grade ML or data solutions.
Strong experience building CI/CD and automation using Jenkins and GitHub.
Strong experience with Airflow (Bash), Bash itself, and Groovy for pipeline automation.
Hands-on configuration automation using Ansible.
Strong coding/scripting capability in Python (including PySpark), plus working knowledge of Spark.
Experience with ML tooling such as MLFlow, TensorFlow, and similar, including model packaging and deployment considerations.
Proven ability to implement observability (metrics/logs/dashboards/alerting), with tooling flexibility (e.g., Grafana, Splunk, or similar).
Comfortable working in hybrid environments; experience with Hadoop and an ability to integrate with cloud services (preference for GCP).

Senior ML Ops/DevOps

Key skills