Machine Learning Engineer

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:

Building, training, and deploying machine learning models efficiently using the managed infrastructure and automation capabilities of AWS SageMaker.
Utilizing Amazon Redshift and S3 for scalable data storage, processing, and comprehensive analysis.
Leveraging Apache Spark and Airflow for large-scale data processing and pipeline orchestration, ensuring smooth data workflows.
Managing and optimizing machine learning workloads on Amazon EMR to improve performance and resource utilization.
Collaborating with data engineers to integrate ML models seamlessly into production environments.
Implementing best practices for model versioning, monitoring, and continuous deployment, ensuring models remain effective and up to date throughout their lifecycle.

What you’ll need to succeed:

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
5+ 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 (e.g., NumPy, Pandas, Scikit-learn)
Hands-on experience with AWS services (EMR, SageMaker, 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).

What’s in it for you:

✔️ Professional growth/ Development