Capco

Senior ML Ops/DevOps

Capco

full-time

Posted on:

Location Type: Hybrid

Location: Warsaw • Poland

Visit company website

Explore more

✨ AI Apply
Apply

Job Level

Senior

Tech Stack

AirflowAnsibleCloudGoogle Cloud PlatformGrafanaGroovyHadoopJenkinsPySparkPythonSparkSplunkTensorflow

About the role

  • 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.

Requirements

  • 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).
Benefits
  • Flexible collaboration model based on a B2B contract
  • Opportunity to work on diverse projects
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
MLOpsDevOpsPlatform EngineeringCI/CDAutomationPythonBashGroovyTensorFlowMLFlow
Soft Skills
mentoringcollaborationcontinuous improvementproblem-solvingcommunication