About the role
You will join a team responsible for building and scaling the ML platform used by 300+ data scientists and ML engineers across 60+ teams. Your work will directly impact how machine learning models are deployed to production — reliably, securely, and at scale.
You’ll spend most of your time implementing and standardising Databricks MLOps (environments, CI/CD pipelines, model monitoring) while also contributing to the evolution of a battle‑tested AWS SageMaker platform.
You day-to-day:
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Design, build, and scale a production-grade ML / MLOps platform
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Implement Databricks MLOps (environments, CI/CD pipelines, monitoring)
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Maintain and improve the AWS SageMaker ML platform
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Automate infrastructure and workflows using Python and Infrastructure‑as‑Code (Terraform)
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Design solutions for a large-scale multi-tenant architecture (60+ teams, 3 AWS accounts)
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Partner closely with data scientists and ML engineers to enable production model deployments
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Ensure platform reliability, security, observability, and cost efficiency
Who we’re looking for:
Nice to have:
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Apache Airflow
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OAuth / Amazon Cognito, API Gateway
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Monitoring tools such as Prometheus, Grafana, New Relic
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Cloud cost optimisation experience
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GitOps tooling (FluxCD, ArgoCD)
Come and join us!
For Poland: In this position you will earn no less that 17 600 PLN gross monthly
Relocation support is not available for this job
Please note that only on-line applications will be taken into consideration.
Only selected candidates will be contacted.
At PMI we run the business in line with ethical principles and encourage SpeakUp culture. We care for equal chances and fair treatment. If you find anything that violates these principles in this job offer or the recruitment process, you may contact our Ethics and Compliance Team at [email protected]. Read more about Ethics&Compliance at PMI – here.
