We're looking for a Machine Learning Engineer to join our AI & Data team at EY in Greece. As a Machine Learning Engineer, you will work with multi-disciplinary teams to support clients in a wide range of data initiatives aiming to generate and present new, useful and actionable insights. You will have the opportunity to work and take responsibilities in challenging engagements, gaining exposure to clients in various sectors both in Greece and abroad.
Requirements
- Bachelor's or Master's degree in computer science, data science, engineering, or a related field.
- Experience in the fields of AIOps, MLOps or System Administration.
- Demonstrated experience in deploying ML models into production environments.
- Participation in Kaggle competitions or personal ML projects can also demonstrate practical skills.
- Experience with cloud platforms like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP) or Cloudera.
- Understanding how to leverage cloud resources for deploying and scaling ML models.
- Proficiency in modern DevOps practices and automated software testing.
- Knowledge of containerization technologies like Docker and container orchestration platforms like Kubernetes.
- Familiarity with AIOps/ MLOps tools and platforms, such as MLflow, Kubeflow, Synapse Analytics or SageMaker.
- Ability to work under tight timelines, in cases for multiple project deliveries.
- Good interpersonal skills and ability to work effectively within high-performing teams.
- Confidence to convey technical advice and guidance to clients.
- Ability to adapt in a fast paced multinational environment.
- Advanced technical writing skills in Greek and English (additional languages will be a plus).
- Self-motivation for continuous development.
- Willingness and ability to travel and work abroad for international projects.
Benefits
- Competitive rewards package
- Cutting-edge technological equipment
- Ticket restaurant vouchers
- Private health and life insurance scheme
- Income protection
- Exclusive EY benefits club card
- Flexible working arrangement (hybrid model)
- Short Fridays, Flex Day and Together Day
- Volunteerism
- Sustainable practices
- Opportunities for creating a positive societal impact