MLOps Engineer (AWS, PySpark, SageMaker)

MLOps Engineer (AWS, PySpark, SageMaker)
Lumicode Sp. z o.o. is part of the Pentacomp Group, a provider of IT solutions and professional IT services for large enterprises and the public sector.
At Pentacomp, we create IT solutions that combine innovation with years of experience — and we have quite a lot of it. We have been operating on the market for nearly 30 years and have successfully delivered numerous projects
– Long-term B2B cooperation
– Remote work model
– International environment and cross-functional teams
– Opportunity to work on large-scale AI and Machine Learning initiatives
– Influence on architecture, tooling, and engineering best practices
As a Data / MLOps Engineer, you will play a key role in designing, building, and optimizing data platforms and Machine Learning infrastructure. You will work closely with Data Scientists, helping bring models from notebooks into production while ensuring scalability, reliability, monitoring, and reproducibility across the entire ML lifecycle.
This role combines Data Engineering, Cloud Engineering, and MLOps, making it ideal for someone who enjoys working across multiple layers of the ML ecosystem.
– Design, build, and maintain end-to-end MLOps platforms and workflows
– Develop automated model training, deployment, and versioning processes
– Build and optimize large-scale data ingestion and processing pipelines using Spark and PySpark
– Deploy and manage ML workloads on AWS, including SageMaker-based solutions
– Implement and maintain CI/CD and Continuous Training (CT) pipelines
– Monitor model performance, data quality, system reliability, and model drift
– Collaborate closely with Data Scientists to productionize ML models
– Implement infrastructure as code and support scalable cloud environments
– Create and maintain technical documentation and architectural decisions
– Solid experience in Data Engineering, MLOps, or Machine Learning Engineering
– Strong Python and PySpark skills
– Hands-on experience building and maintaining large-scale data pipelines
– Practical experience with Amazon SageMaker and production ML deployments
– Strong knowledge of Apache Spark
– Experience working with AWS cloud services
– Good understanding of CI/CD practices and deployment automation
– Ability to translate Data Science prototypes into production-ready solutions
– Understanding of different Machine Learning models, their strengths, limitations, and monitoring approaches
– Strong SQL skills
– Experience with Feature Stores, experiment tracking, or model registries
– Knowledge of AWS CDK, Terraform, or CloudFormation
– Experience with PyTorch
– Familiarity with observability and monitoring solutions for data and ML platforms
– Experience working in Agile environments