Data/MLOps Engineer

co.brick talents — powered by AI, powered by people. Data/MLOps Engineer (CT&C Engineering) For our Client, we are looking for a Data/MLOps Engineer to join their CT&C Engineering team. In this role, you will bridge the gap between data science and production, ensuring that scalable data solutions provide efficient ingestion, transformation, storage, and real-time analysis. If you have a strong background in ML, solid PySpark skills, and know AWS SageMaker inside out, this role is for you! Quick Job Details Rate: 140 – 150 PLN/h net Form of Cooperation: B2B Contract Start Date: ASAP English: Minimum B2 level Who Our Client Is Looking For We need a technical expert who brings overall ML background knowledge and can specifically address these core needs: The Bridge to Production: You can confidently face off with Data Scientists (who often produce notebooks only) and successfully implement their work into production-quality models. ML Model Expertise: You understand different ML models, know how to monitor them, and clearly understand their pros and cons. Hands-on Implementation: You are technically capable of building and executing these solutions using PySpark and AWS SageMaker. Technical Stack Languages & Frameworks: Python, PySpark, PyTorch, SQL Data Processing: Apache Spark, ETL/ELT Cloud & Infrastructure: AWS CDK, AWS Lambdas, AWS SageMaker, Terraform / CloudFormation Methodology & Tools: Agile, CI/CD, Training Design Key Responsibilities 1. ML & Data Infrastructure Deploy and maintain end-to-end ML lifecycles (automated training, deployment, and versioning). Build and support core MLOps components like Feature Stores, experiment tracking, and model registries. Manage scalable cloud infrastructure using Infrastructure as Code (IaC) and develop robust CI/CD/CT (Continuous Training) pipelines. 2. Data Engineering & Pipeline Optimization Build high-volume ingestion and processing pipelines using Apache Spark and PySpark. Implement data models and storage optimizations for low-latency inference and high-performance analytics. Integrate automated data quality checks and observability. 3. Governance, Security & Collaboration Proactively monitor model drift, data quality, and system latency. Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility. Operate within an Agile framework, collaborate with Data Scientists and Product Owners, and provide clear technical documentation.