We are looking for an experienced Senior Data/ML Engineer to drive the development, deployment, and optimization of large-scale Machine Learning and Big Data solutions. You will work end-to-end across the ML lifecycle, build distributed data pipelines, and shape our MLOps best practices within a modern AWS environment.

Hybrid: Trójmiasto / Łódź / Warsaw | Python - Spark - AWS - MLOps | Full-time

Senior Data / ML Engineer

Your responsibilities

• Own the full lifecycle of ML models – from development and deployment to monitoring and continuous improvements.
• Implement MLOps principles, including CI/CD, automation, testing, and observability for ML workloads.
• Build and maintain data ingestion, processing, and transformation pipelines (batch & streaming) using Python and Apache Spark.
• Design and optimize distributed, highly parallel Big Data pipelines processing massive datasets in near real-time.
• Use Spark to enrich and prepare corporate data for search, analytics, and advanced ML use cases.
• Closely collaborate with Data Scientists, DevOps Engineers, and IT teams to deliver production-grade ML solutions.
• Work with analysts and business stakeholders to develop and refine analytical models.
• Enhance and extend the organization’s MLOps frameworks and libraries, ensuring scalability across multiple ML use cases.
• Explore and evaluate cloud-native AI/ML solutions on AWS.

Our requirements

• 5+ years of hands-on experience with Python and Apache Spark.
• Strong experience with AWS services, especially S3, Glue, SageMaker, Lambda, Step Functions / Airflow / MWAA.
• Practical knowledge of AWS automation using AWS CLI, boto3, IAM roles.
• Solid understanding of algorithms, data structures, statistics, and linear algebra.
• Experience with training and deploying ML models using TensorFlow or PyTorch.
• Understanding of distributed systems and Big Data technologies (Hadoop, Hive, or equivalents).
• Proficiency in SQL (Spark SQL / Hive SQL) and experience building production-grade data pipelines.
• Strong Git skills (Bitbucket, branching workflows, code review).
• Experience working in Agile/SAFe environments.
• Experience with ML models such as propensity modeling, CLV, micro-segmentation, recommendation engines.
• Experience supporting business teams during model development and deployment processes.