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Middle Machine Learning Engineer

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Angajator: Hipo Imports
Domeniu:
  • IT Software
  • Tip job: full-time
    Nivel job: 1 - 5 ani experienta
    Orase:
  • BUCURESTI
  • Actualizat la: 13-02-2026
    Remote work: On-site

    PPC Energy

    Global Digital Solutions

    Bucharest

    Unlimited period

    #YourCareer starts with doing what you like

    We are always looking for talented and motivated colleagues to join our team and together, contribute to the creation of a sustainable future, based on inclusion, empathy, respect and equal opportunities.

    As a Middle Machine Learning Engineer, you will focus on designing, developing and deploying machine learning models that drive impactful business decisions. You will be responsible for building end-to-end pipelines, ensuring that models are production-ready, scalable, and integrated seamlessly into existing systems. Your work will directly influence key business metrics, driving innovation and operational efficiency.

    Together with us, you have the chance to grow everyday, contributing to energy transition, being responsible of:

    • Designing, building, and deploying machine learning models using Python and frameworks such as TensorFlow, PyTorch, ensuring models are robust, scalable, and optimized for performance.

    • Utilizing SageMaker's built-in algorithms and tools to streamline workflows

    • Developing and managing big data processing workflows using Apache Spark on AWS EMR. Optimize data processing tasks to handle large-scale datasets efficiently, enabling fast feature extraction and preprocessing for machine learning models.

    • Utilizing Amazon Redshift and S3 for data storage, processing, and analysis

    • Using MLFlow for experiment tracking, model management, and versioning, and ensure a systematic approach to deploying and monitoring models in production.

    • Utilizing Apache Airflow to schedule and automate data processing tasks and model training workflows.

    • Collaborating with data engineers to ensure seamless integration of ML models into production environments.