Data / MLOps Engineer (m/f/n)

SHIMI Sp. z o.o.

• Ability to interface with data science teams producing notebooks and implement production-quality models.
• Understanding of different ML models, their monitoring, and pros and cons.
• Technical proficiency in PySpark and AWS Sagemaker.
• Overall background knowledge of ML concepts and operations.

ML & Data Infrastructure:

• Deploy and maintain end-to-end ML lifecycles, focusing on automated training, deployment, and versioning.
• Build and support core MLOps components, including Feature Stores, experiment tracking, and model registries.
• Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC) (Terraform or CloudFormation).
• Develop robust CI/CD/CT (Continuous Training) pipelines to ensure seamless, repeatable production releases.

Data Engineering & Pipeline Optimization:

• Build high-volume ingestion and processing pipelines using Apache Spark and Python/PySpark.
• Implement data models and storage optimizations to support low-latency inference and high-performance analytics.
• Integrate automated data quality checks and observability to ensure system health and reliability.

Governance & Security:

• Implement proactive monitoring for model drift, data quality, and system latency.
• Maintain strict versioning for data, code, and artifacts to guarantee 100% reproducibility.
• Apply security best practices, ensuring data privacy and access control across the entire ML lifecycle.

Collaborative Delivery:

• Operate within an Agile framework: define technical tasks, manage tickets, and provide updates in daily stand-ups.
• Collaborate with Data Scientists and Product Owners to translate requirements into technical specifications.
• Provide clear documentation for workflows, pipelines, and architectural choices.