About the company
At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Being part of EY in Greece means being part of a continuously growing team, which has been announced as Top Employer for the fourth consecutive year, certified as a Great Place to Work for the third year in a row, and awarded as Best Workplace in Professional Services & Consulting for the second consecutive time, for our almost 3,000 professionals!
Responsibilities
- Collaborating with data scientists on model integration and deployment.
- Setting up infrastructure and tools for deploying and monitoring models.
- Managing cloud resources and optimizing performance.
- Implementing CI/CD pipelines for automated testing and deployment.
- Building and maintaining data pipelines for pre-processing and transformation.
- Monitoring model performance and system health.
- Ensuring data security and compliance.
- Documenting processes and best practices.
- Staying updated with advancements in MLOps/ AIOps and contributing to EY innovation.
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.
- Ideally, you’ll also have: Proficiency in modern DevOps practices and automated software testing. Knowledge of containerization technologies like Docker and container orchestration platforms like Kubernetes is valuable.
- Familiarity with AIOps/ MLOps tools and platforms, such as MLflow, Kubeflow, Synapse Analytics or SageMaker, is advantageous. Understanding how to set up and manage machine learning pipelines, model monitoring, and deployment infrastructure is important.
- Knowledge of infrastructure components like servers, storage systems, and networking. Understanding how to provision and configure resources for ML workloads.
Conditions
- Developing your professional growth: You'll have unlimited access to educational platforms, EY Badges and EY Degrees, alongside support for certifications. You will experience personalized coaching and feedback, and gain exposure to international projects, through our expansive global network, empowering you to define and achieve your own success.
- Dive into our innovative GenAI ecosystem, designed to enhance your EY journey and support your career growth. These advanced AI tools will empower you to focus on higher-value work and meaningful interactions, enriching your professional experience like never before.
- Empowering your personal fulfilment: We focus on your financial, social, mental and physical wellbeing.
- Our competitive rewards package, depending on your experience, includes cutting-edge technological equipment, ticket restaurant vouchers, a private health and life insurance scheme, income protection and an exclusive EY benefits club card that provides a wide range of discounts, offers and promotions.
- Our flexible working arrangement (hybrid model) is defined based on your own preferences and team’s needs, and we enjoy various getaways such as short Fridays, Flex Day and Together Day.