Regular AI/ML Ops Engineer

Technologie, których używamy

O projekcie

Twój zakres obowiązków

• Set up Databricks clusters, jobs, and workflows for ML scoring use cases.
• Implement scalable infrastructure capable of running thousands of ML scoring tasks.
• Develop deployment processes for ML models using Databricks MLflow or equivalent.
• Build frameworks to orchestrate scoring of >1,500 ML models or scoring jobs.
• Integrate logging, alerting, and dashboards to monitor scoring throughput.
• Work alongside Dev Ops Engineers to automate provisioning and deployments.
• Define operational SLAs for scoring workloads.
• Ensure role-based access control and credential management.
• Implement policies for secure handling of model artifacts and scoring data.

Nasze wymagania

• Azure knowledge.
• Experience with Databricks, MLflow, Jobs, Workflows, Delta Lake.
• MLOps skills.
• Python knowledge for workflow scripting.
• Terraform skills for Infrastructure as Code.
• Proven ML Ops experience in production ML environments.
• Experience in CI/CD pipelines for ML model deployment (e.g., Azure DevOps, GitHub Actions).
• Proficiency with monitoring tools (e.g., Datadog, Prometheus, Grafana).
• Knowledge of model lifecycle management, versioning, and reproducibility.
• Understanding of Infrastructure as Code and cloud environment automation.Knowledge of model lifecycle management, versioning, and reproducibility.
• English Level: B2
• Hybrid work from client's office in Warsaw/Poznań/Lublin

Mile widziane

• Experience executing high-volume ML scoring in Databricks.
• Familiarity with industry best practices for regulated environments.
• Knowledge of job queueing systems and parallel execution patterns.
• Awareness of cost optimisation for ML scoring infrastructure.
• Exposure to Azure Databricks and the Azure ecosystem.
• German language knowledge.