ML Platform Engineer, Contractor

This is a fully remote position, open to applicants in Poland.

📋 Description

• Develop and sustain end-to-end MLOps automation, including CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance.

• Design and manage model serving infrastructure, covering deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and optimising latency/throughput.

• Create and oversee training and experimentation infrastructure, focusing on job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tools.

• Implement observability for machine learning systems, incorporating data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows.

• Develop and manage data pipelines for ingestion, transformation, feature engineering, and export across diverse sources and destinations.

• Design and maintain a feature store or feature platform layer, ensuring serving consistency, point-in-time accuracy, and reusability across teams.

• Provide well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can depend on.

• Enforce secure data handling practices and adhere to relevant data protection standards, access controls, and audit requirements.

• Contribute to documentation, platform standards, and the continuous enhancement of ML engineering processes across teams.

⛳️ Requirements

• A Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).

• Over 5 years of experience in Data or ML Engineering, with at least 3 years dedicated to deploying ML systems in production.

• Proficient in Python (typed code, async, testing) and have strong SQL capabilities.

• Practical MLOps experience, including model registries, experiment tracking (MLflow or Vertex Experiments), pipeline orchestration, and reproducible training runs.

• Strong understanding of DevOps principles, including CI/CD (GitHub Actions, Cloud Build, or similar), IaC (Terraform), and containerization (Docker).

• Familiarity with at least one major cloud provider (GCP, AWS, Azure) and experience in deploying data solutions in the cloud.

• Experience in building and maintaining data pipelines using orchestrators (Airflow/Composer, Dagster) and distributed engines (Spark, BigQuery).

• A strong troubleshooting mindset, with the ability to debug issues across data, infrastructure, pipelines, and deployments.

• A collaborative attitude and effective communication skills with engineering, analytics, and business stakeholders.

🏝️ Benefits

• Annual salary reviews, promotions, and performance bonuses.

• Access to myPOS Academy for skill development and training.

• Unlimited access to courses available on LinkedIn Learning.

• An annual individual training and development budget.

• A referral bonus for bringing friends on board, as we know working with friends is enjoyable.

• Participation in team-building activities, social events, and networks at an international level.