Job Description – Machine Learning Engineer

Location: Bucharest

Work Setup: Hybrid

Job Summary:

As a 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:

• Design, build, and deploy machine learning models using Python and frameworks such as TensorFlow, PyTorch, ensuring models are robust, scalable, and optimized for performance.
• Utilize SageMaker’s built-in algorithms and tools to streamline workflows
• Develop and manage big data processing workflows using ApacheSpark on AWS EMR. Optimize data processing tasks to handle large-scale datasets efficiently, enabling fast feature extraction and preprocessing for machine learning models.
• Utilize Amazon Redshift and S3 for data storage, processing, and analysis
• Use MLFlow for experiment tracking, model management, and versioning, and ensure a systematic approach to deploying and monitoring models in production.
• UtilizeApache Airflow to schedule and automate data processing tasks and model training workflows.
• Collaborate with data engineers to ensure seamless integration of ML models into production environments.

What you’ll need to succeed: 

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 2+ years of experience in machine learning engineering, including demonstrated experience building and deploying machine learning models at scale.
• Strong programming expertise in Python and experience with Spark for distributed data processing.
• Hands-on experience with AWS services (EMR, SageMaker, Bedrock, Redshift, S3) for end-to-end machine learning pipelines.
• Ability to write clean, maintainable, and efficient production-level code, adhering to best practices in software engineering (e.g., version control, testing, CI/CD).
• Experience using MLFlow or similar tools for managing the machine learning lifecycle (model tracking, versioning, and governance).
• Familiarity with Apache Airflow (or equivalent tools) for managing and automating complex machine learning workflows.
• Strong analytical and problem-solving skills.
• Strong communication and collaboration skills.
• Good English knowledge (upper intermediate level).