Machine Learning Engineer
Machine Learning Engineer
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Job Description
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 endtoend pipelines ensuring that models are productionready 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 efficientlyusing the managed infrastructure and automation capabilities of AWS SageMaker.
- Utilizing Amazon Redshift and S3for scalable data storage processing and comprehensive analysis.
- Leveraging Apache Spark and Airflowfor largescale data processing and pipeline orchestration ensuring smooth data workflows.
- Managing and optimizing machine learning workloadson Amazon EMR to improve performance and resource utilization.
- Collaborating with data engineersto 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 youll need to succeed:
- Bachelors or Masters 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 Scikitlearn)
- Handson experience with AWS services (EMR SageMaker Redshift S3 for endtoend machine learning pipelines.
- Ability to write clean maintainable and efficient productionlevel 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 problemsolving skills.
- Strong communication and collaboration skills.
- Good English knowledge (upper intermediate level).
Whats in it for you:
Professional growth/ Development
- opportunities for professional and personal development
- dynamic work environment and different business lines
- international work environment
- internal mobility programs
- courses coaching sessions mentoring
Rewards
- medical subscription/medical insurance
- meal vouchers bonuses (Energeticians Day Easter Christmas etc.
- 13th salary
- reimbursement of part of the holiday ticket
- extra free days (Birthday Energeticians Day etc.
- special offers from our collaborators
- referral employee program
- Bookster
Way of working
- hybrid way of working (office & smartworking)
- short Friday
Employment Type
Full-Time
