Data & AI Systems and Platforms Engineer

As a Data & AI Systems and Platforms Engineer, your key responsibilities include designing system architectures for Data and AI platforms, deploying and configuring these systems, and providing ongoing maintenance and support. You'll work closely with project managers and clients to ensure the solutions fit their needs and you'll be involved in troubleshooting, upgrades, and documentation improvement.

To qualify for the Data & AI Systems and Platforms Engineer role, candidates must possess a BSc in Information Technology or a related field, with an MSc being a plus. Additionally, 3-5 years of relevant experience in systems and platforms engineering or a similar project-based environment is crucial. Knowledge of cloud deployments, DevOps practices, and programming frameworks such as Python or Java is also important.

Technical skills essential for the Data & AI Systems and Platforms Engineer role include expertise in operating systems like Windows and Linux, familiarity with cloud technologies (Azure, AWS), and experience with microservices and Kubernetes. Proficiency in SQL Server, PostgreSQL, and real-time streaming data technologies like Kafka will significantly enhance your candidacy.

Collaboration is vital for the Data & AI Systems and Platforms Engineer. You will work alongside project managers, implementation teams, and clients to outline project requirements, troubleshoot issues, and ensure that the solutions you develop are aligned with the clients' overall vision and goals.

Ideal candidates for the Data & AI Systems and Platforms Engineer role possess strong attention to detail, accountability, and self-motivation. Innovative thinking and the ability to work both independently and within a larger team are also highly valued, as is the ability to communicate effectively in both Greek and English.

Highlight specific projects where you designed, deployed, or maintained Data and AI platforms. Discuss the technologies used, the challenges faced, and how you overcame them to demonstrate your practical experience.

Microservices allow for scalable and flexible architectures that are essential in managing complex data operations. Discuss how microservices improve deployment efficiencies, facilitate continuous integration, and enable responsive architecture.

Explain your systematic approach to troubleshooting, accentuating techniques like log analysis, system monitoring, and client communication to identify and resolve issues efficiently.

Discuss the importance of clear documentation in facilitating team collaboration and future development. Provide examples of documentation tools you’ve used and the specifics of what you typically include.

Mention credible sources such as tech conferences, webinars, online courses, and relevant publications that you follow. Emphasizing a continuous learning mindset will demonstrate your commitment to staying ahead in the industry.

Share specific details about the client’s requirements and the steps you took to ensure their needs were met. Highlight your communication skills and how you built a relationship of trust and transparency during the project.

Discuss criteria such as scalability, security, performance, and maintainability. Explain how you prioritize these factors depending on project requirements and client goals.

Mention a combination of cloud management tools and configuration management platforms you have worked with, providing context on how they enhance deployment efficiencies and team collaboration.

Outlining a specific instance where you faced a setback, explain your thought process, the actions taken to address it, and the ultimate outcome, showcasing your problem-solving skills.

Tailor your response by reflecting on your unique skill sets, experiences, and passion for technology. Emphasize the alignment between your previous roles and projects with the requirements of this position.