Data/MLOps Engineer

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.