AI Platform Engineer (Python OR Java)

AI Platform Engineers at Velotio are responsible for a variety of tasks that include requirement gathering, coding, testing, CI automation, and production deployment. They work extensively with algorithms, focus on machine learning models, and integrate applications with APIs to deliver efficient solutions.

The ideal candidate for the AI Platform Engineer position at Velotio should have at least 7 years of relevant experience, a strong coding background in Python or Java, and familiarity with platforms like Kubernetes or Terraform. A knowledge of machine learning, deep learning, and data mining is also critical.

AI Platform Engineers at Velotio should be knowledgeable in both cloud-native and cloud-agnostic technologies. This includes working with AWS services like S3 and Lambda, as well as tools like Kubernetes, Grafana, and data pipeline technologies like Apache Kafka or AWS Glue.

The work culture for AI Platform Engineers at Velotio is one of empowerment and autonomy. The company boasts a flat hierarchy, fast decision-making, and a fun environment where success is regularly celebrated. Velotio is dedicated to fostering diversity and an inclusive workplace.

Velotio emphasizes personal ownership and rapid growth for its AI Platform Engineers. The company encourages engineers to take initiative and provides a positive environment that nurtures learning and development through hands-on experience with innovative technologies.

When answering this question, emphasize specific projects where you successfully deployed machine learning models, including the technologies you used and any challenges you faced during deployment. Highlight your role in operationalizing these models in a production environment.

Detail your experience with Continuous Integration and Continuous Deployment processes, mentioning specific tools you’ve used, such as GitHub Actions or Jenkins. Explain how you utilized these processes to streamline workflows and improve deployment efficiency.

Discuss strategies you implement to ensure scalability, such as designing modular architectures, utilizing cloud services for load balancing, and employing distributed computing techniques. Give examples from your past experience where you scaled a solution successfully.

Explain how data is foundational to AI solutions, from gathering quality datasets to performing preprocessing and feature selection. Refer to your experience in building data pipelines and managing data in various formats or cloud environments.

Provide a structured approach to addressing performance issues, including monitoring metrics, analyzing model behavior, and experimenting with different algorithms or data inputs. Share examples of how you have identified and solved similar issues in the past.

Discuss the version control systems you’ve worked with, emphasizing how you implemented best practices for maintaining code integrity. Mention experiences with branching, merging, and collaboration in tools like Git.

Explain which cloud platforms you’re familiar with, detailing specific services you’ve utilized for AI development. Discuss how these services helped you in creating scalable and efficient machine learning solutions.

Mention specific resources you utilize to stay updated on AI trends, including online courses, research papers, tech conferences, or professional communities. Highlight any relevant certifications you have pursued or projects you've undertaken to deepen your knowledge.

Share a detailed example of a technical challenge you encountered, the steps you took to analyze and resolve the issue, and the outcome. This will showcase your problem-solving capabilities and your persistence to find effective solutions.

Articulate your thoughts on the importance of aspects such as data quality, algorithm selection, model validation, or user experience. Ground your beliefs in facts and experiences to demonstrate a thoughtful approach toward AI development.