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December 19, 2025

ML/Data Engineer

Senior • Hybrid

$140 - $170/hr

Łódź, Poland , +3

Project information:

  • Industry: banking

  • Rate: up to 170 zł/h net + VAT

  • Location: Gdańsk, Gdynia, Warsaw, or Łódź

  • Hybrid role: 40% remote, 60% onsite


Summary: The primary objective of the Senior Data/ML Engineer role is to oversee the complete lifecycle of machine learning models and enhance MLOps practices to deliver high-quality machine learning solutions. This includes development, deployment, and monitoring to ensure optimal performance and reliability within the organization.

Main Responsibilities:

  • Manage the lifecycle of machine learning models from development to deployment and monitoring.

  • Implement MLOps principles, including continuous integration, continuous delivery, testing, and monitoring.

  • Work with Spark & Python to maintain data ingestions and transformations, handling real-time and batch data processing.

  • Build distributed and highly parallelized big data processing pipelines for massive data in near real-time.

  • Leverage Spark for data enrichment and transformation to enable advanced analytics.

  • Collaborate with cross-functional teams to deliver machine learning solutions.

  • Develop analytics models in partnership with analysts and stakeholders.

  • Optimize MLOps practices and explore cloud solutions in AI/ML areas.

Key Requirements:

  • Minimum 5 years proficiency in Python & Spark.

  • Hands-on experience with AWS services (S3, Glue, SageMaker, Lambda).

  • Practical understanding of AWS Infrastructure and automation using CLI, boto3, and IAM roles.

  • Understanding of algorithms, data structures, statistics, and linear algebra.

  • Experience with machine learning frameworks (TensorFlow or PyTorch).

  • Solid understanding of distributed systems (Hadoop/Hive ecosystem).

  • Proficient in SQL (Spark/Hive SQL).

  • Experience with code versioning tools (BitBucket, GIT).

  • Familiar with Agile/Safe framework.

Nice to Have:

  • Design and implementation of ML Models (e.g., Propensity, Customer Lifetime Value).