The role involves designing MLOps platforms, implementing CI/CD pipelines, monitoring model performance, and collaborating with data scientists to productionize ML models. Mentorship to juniors is encouraged.
CAPCO POLAND
*We are looking for Poland based candidate. Hybrid work. Warsaw is preferred.
At Capco Poland, we’re not just another consultancy - we’re the spark behind digital transformation in the financial world. As a global leader in technology and management consulting, we thrive on helping clients tackle the toughest challenges across banking, payments, capital markets, wealth, and asset management.
Our secret?
A culture that’s fast, flexible, and fiercely entrepreneurial. We move quickly, think creatively, and always put our people first.
We’re passionate about growth - both for our clients and ourselves - and that means attracting the very best talent to join us on this exciting journey.
We’re proud to be:
• Trailblazers in banking, payments, capital markets, wealth, and asset management
• Champions of an agile, nimble, and innovative work environment
• Dedicated to building a team of top-notch professionals who share our drive and vision
• Trailblazers in banking, payments, capital markets, wealth, and asset management
• Champions of an agile, nimble, and innovative work environment
• Dedicated to building a team of top-notch professionals who share our drive and vision
THINGS YOU WILL DO
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.
SKILLS & EXPERIENCES YOU NEED TO GET THE JOB DONE
- 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).
Nice to have:
- 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.
ONLINE RECRUITMENT PROCESS STEPS
- 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.
Capco Toronto, Ontario, CAN Office
First Canadian Place, Suite 5010, 100 King Street West, Toronto, Ontario, Canada, M5X 1C7
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