AI Engineer

To be considered for the AI Engineer position at Maneva, you should hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field. Ideally, you would have hands-on experience with computer vision applications, coupled with a familiarity with MLOps and relevant tools. Certifications or coursework in AI model development can enhance your application and help you stand out.

As an AI Engineer at Maneva, you'll focus on developing and training vision-based AI applications tailored for manufacturing. Core responsibilities will include building and managing deployment pipelines for AI/ML models, monitoring their performance, troubleshooting any issues, and collaborating closely with other teams to ensure alignment with manufacturing goals. Your role will be crucial in ensuring our AI solutions achieve real-world impact.

In your role as an AI Engineer at Maneva, you will work with a variety of cutting-edge technologies, including Python, Docker, and cloud platforms such as AWS, Azure, and GCP. Proficiency in Nvidia-based environments and ML libraries like PyTorch or TensorFlow is vital, as well as experience in MLOps tools. You'll also have hands-on opportunities to train neural networks and use monitoring tools for deployed models.

While direct experience in manufacturing or industrial systems is a plus for the AI Engineer role at Maneva, it is not a strict requirement. What we value more is your hands-on experience with AI/ML model development and your passion for using technology to solve real-world problems. If you have a strong background in computer vision or MLOps, you could be a great fit.

The interview process for the AI Engineer position at Maneva will likely include a combination of technical assessments and behavioral interviews. You may be asked to demonstrate your understanding of AI/ML concepts, your hands-on experience with applicable technologies, and how you've tackled challenges in past roles. Be prepared to discuss your projects in detail and how they relate to manufacturing applications.

At Maneva, we foster an environment that encourages professional growth and career advancement. As an AI Engineer, you will have the chance to work on innovative projects that have a significant impact, and you’ll be supported in learning new skills and technologies. Opportunities to lead projects or mentor others will also arise as our startup continues to grow.

Occasionally, travel may be required for the AI Engineer position at Maneva to support integration and deployment efforts at customer sites. While this may not be frequent, being open to travel will be beneficial in ensuring that our AI solutions are effectively integrated into client operations.

When answering this question, focus on a specific project that showcases your problem-solving skills and technical expertise. Discuss the problem you were trying to solve, the approach you took, the technologies you used, and the results achieved. Emphasize how your contributions led to a positive outcome.

Share specific examples of computer vision projects you've worked on, touching on tasks such as image classification, object detection, or segmentation. Explain the technologies used, challenges faced, and how you overcame them. This helps demonstrate your hands-on expertise in the area.

Highlight your understanding of monitoring tools and processes that you use to assess model performance in real-time. Discuss how you identify potential issues, what metrics you track, and how you perform regular updates and troubleshooting to maintain model reliability.

When answering, detail your experience with CI/CD pipelines for ML models. Talk about the specific tools and practices you follow, such as Git for version control, Docker for containerization, and the importance of automated testing. Showcase your ability to streamline and optimize deployment processes.

Discuss your familiarity with various MLOps tools you've used in past projects. Explain how these tools have helped you manage model lifecycle, deployment, and monitoring effectively. Provide examples of situations where you successfully leveraged these tools to improve productivity and model performance.

Provide details about a specific project where you worked in cross-functional teams, highlighting the roles you collaborated with and the outcome. Share how you communicated effectively with team members from different backgrounds and integrated diverse perspectives into the project.

Indicate your proficiency in relevant programming languages such as Python, and discuss its application in your AI/ML projects. Highlight any libraries or frameworks you've utilized (like TensorFlow or PyTorch) and explain how they contributed to your project outcomes.

Choose a relevant challenge and explain why it was significant. Discuss your analytical approach, the steps you took to resolve the issue, and what you learned from the experience. This demonstrates your resilience and problem-solving capability.

Talk about your experience with collecting, cleaning, and preparing datasets for training. Emphasize the importance of data quality and the techniques you employ for data curation, such as data augmentation or normalization, and how they affect the model's performance.

Reflect on your interests and motivations, focusing on how you see AI transforming the manufacturing industry. Discuss your passion for technology and its real-world applications, as well as your eagerness to contribute to innovative solutions in this space.