Senior ML Ops/Devops - Hybrid (She/He/They)

Platform Engineering for ML

• 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.

Model deployment & release engineering

• 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.

Monitoring, observability & operational readiness

• 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).

Collaboration & stakeholder management

• 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).

• Exposure to GCP services, especially Vertex AI and BigQuery.
• Experience with secrets management (tooling flexible).
• Familiarity with Model Risk Management, SDLC controls, and data lineage concepts in regulated environments.
• Prior experience mentoring junior engineers and helping teams adopt standard patterns.

• Screening call with the Recruiter
• Capco Hiring Manager Interview
• Client’s interview
• Feedback/Offer

We offer a flexible collaboration model based on a B2B contract, with the opportunity to work on diverse projects.