Senior MLOps / ML Platform Engineer
New
S
Sigma SoftwareAdTech
Warszawa, Emilii Plater 53, Poznań, Zwierzyniecka 3, Kraków, Wadowicka 7, Warszawa, Country code: PLFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- Upper-Intermediate English level or higher
- Experience
- 5+ years
- Required Skills
- DockerPythonGCPKubeflowKubernetesMLFlowAirflowCI/CDTerraform
Requirements
- 5+ years of experience in MLOps, ML platform engineering, or infrastructure engineering supporting production ML systems.
- Strong Python skills and experience building platform-level tooling and automation.
- Hands-on experience with Kubernetes and Docker.
- Experience building CI/CD pipelines for ML workloads.
- Hands-on production experience with MLflow, Kubeflow, Airflow, Argo Workflows, Vertex Pipelines, or similar orchestration and ML lifecycle platforms.
- Strong understanding of ML observability including drift detection, train/serve skew monitoring, and incident response.
- Experience designing or supporting multi-tenant ML systems and isolated model environments.
- Experience working with cloud platforms, preferably GCP.
- Experience with infrastructure-as-code tools such as Terraform.
- Experience with Linux environments.
- Understanding of the ML lifecycle and productionization processes.
- Upper-Intermediate English level or higher.
Responsibilities
- Build and maintain ML training orchestration pipelines across hourly, daily, and weekly schedules.
- Implement retries, backfills, and idempotent execution mechanisms.
- Design and support model registry workflows including versioning, lineage, evaluation gates, and promotion processes.
- Develop isolated per-advertiser model environments with namespace and configuration separation.
- Build scalable refresh pipelines and publishing workflows for serving infrastructure.
- Implement shadow mode and champion/challenger deployment strategies.
- Develop monitoring and alerting for ML-specific metrics including feature drift, prediction drift, train/serve skew, and calibration decay.
- Ensure reproducibility of ML workflows using containerized environments, pinned dependencies, and data snapshots.
- Monitor training and scoring costs across tenants.
- Collaborate with DevOps and SRE engineers on CI/CD and infrastructure automation.
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