ML Platform Engineer – Contractor

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

📋 Description

• Develop and sustain comprehensive MLOps automation from start to finish: CI/CD for models and pipelines, management of environments, versioning of artifacts (models, data, prompts, code), and governance of releases.

• Design and manage model serving infrastructure: deployment strategies (blue/green, canary, shadow), management of endpoints, scaling, and optimization of latency/throughput.

• Create and uphold training and experimentation infrastructure: orchestration of jobs, provisioning of compute resources, tracking of experiments, management of hyperparameters, and tools for reproducibility.

• Establish observability for machine learning systems: checks on data quality, detection of feature drift, monitoring of model performance, assessments for bias, as well as workflows for alerting and incident response.

• Build and maintain data pipelines for the ingestion, transformation, feature engineering, and export of data across various sources and destinations.

• Design and oversee a feature store or feature platform layer: ensuring consistency in serving, point-in-time accuracy, and reuse among teams.

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

• Ensure secure data handling and compliance with pertinent data protection regulations, access controls, and auditing requirements.

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

⛳️ Requirements

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

• Over 5 years of experience in Data or ML Engineering, with a minimum of 3 years in deploying ML systems to production.

• Proficient in Python (typed code, async, testing) and possesses solid SQL skills.

• Practical MLOps experience: model registries, experiment tracking (using MLflow or Vertex Experiments), orchestration of pipelines, and reproducible training runs.

• Strong foundation in DevOps: CI/CD (GitHub Actions, Cloud Build, or similar), Infrastructure as Code (Terraform), and containerization (Docker).

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

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

• Strong troubleshooting skills: the ability to debug issues across data, infrastructure, pipelines, and deployments.

• A collaborative mindset with clear communication skills among engineering, analytics, and business stakeholders.

🏝️ Benefits

• Annual reviews of salary, promotions, and performance-based bonuses.

• Access to myPOS Academy for skill enhancement and training.

• Unlimited enrollment in courses on LinkedIn Learning.

• An annual budget dedicated to individual training and development.

• A referral bonus for bringing friends on board, as we believe that working alongside friends is enjoyable.

• Opportunities for team-building, social events, and networking on a global scale.