Hybrid Senior ML Ops/DevOps

at Capco

• Job title: Senior ML Ops/DevOps
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: No Education Requirement
• Tech skills: Airflow, Ansible, Cloud, Google Cloud Platform, Grafana, Groovy, Hadoop, Jenkins, PySpark, Python, Spark, Splunk, Tensorflow
• Location requirements: Hybrid, Warsaw, Poland

About the role

• Senior ML Ops/DevOps developing MLOps platform components at Capco Poland for financial digital transformation. Responsibilities include CI/CD, model deployment, monitoring, and team collaboration.

Responsibilities

• 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