AI Engineer

To become an AI Engineer at Maneva, candidates should hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Engineering, or a closely related field. Relevant certifications in MLOps or AI model development are beneficial. Additionally, practical experience in MLOps, AI/ML model development, and computer vision applications in real-world settings is essential.

As an AI Engineer at Maneva, you’ll primarily work on developing vision-based AI applications tailored for manufacturing. Your projects will involve tasks such as classification, object detection, and segmentation, aimed at enhancing production efficiency and safety. You'll also be involved in building AI/ML deployment pipelines, setting up MLOps tools, and monitoring model performance.

Candidates applying for the AI Engineer role at Maneva should have proficiency in Linux, Python, Docker, and Git. Experience working in Nvidia-based environments (CUDA, TensorRT) is crucial. Familiarity with cloud platforms used for AI services (AWS, Azure, GCP) and knowledge of CI/CD pipelines is also important, as these skills will be critical in deploying and managing AI models.

Absolutely! Maneva prides itself on fostering an environment for professional growth. As an AI Engineer, you will have access to cutting-edge AI tools and the chance to collaborate with industry experts, which will provide numerous learning opportunities. You can expect to gain exposure to real-world applications and be a part of impactful projects which are both personally and professionally rewarding.

Yes, the AI Engineer role at Maneva may occasionally involve traveling to customer sites. This travel is primarily to support integration and deployment efforts, allowing you to work closely with clients and ensure smooth operational transitions.

Maneva offers a collaborative and supportive work environment for AI Engineers. You will be part of a dynamic team dedicated to innovation, where your contributions are valued, and your ideas can lead to real-world transformation in the manufacturing sector. We emphasize teamwork and promote an atmosphere that encourages continuous learning and improvement.

As an AI Engineer at Maneva, you will be working with advanced AI and MLOps tools specifically designed to revolutionize manufacturing operations. You will engage with ML libraries such as PyTorch, TensorFlow, Keras, and SKlearn, while applying your skills in Docker, CI/CD, and monitoring tools to optimize and maintain AI models.

When framing your response, highlight specific projects where you developed AI applications that addressed manufacturing challenges. Discuss your methodology, the models used for classification, object detection, or segmentation, and the impact these applications had on operational efficiency.

In your answer, emphasize the importance of setting clear performance metrics, utilizing monitoring tools to track model output, and how you would incorporate feedback loops to iteratively enhance the model's efficiency. Illustrate with examples of how you’ve tackled performance issues in the past.

Discuss the systematic steps you take to identify issues, from examining data inputs to verifying model architecture. Explain your process in testing hypotheses and how you leverage tools for performance analysis and logging to ensure reliable results.

Highlight specific MLOps tools you’ve worked with—be them for continuous integration/continuous deployment (CI/CD) or model management. Share examples of how you've implemented MLOps processes in your previous roles to improve efficiency and reduce deployment times.

Provide a narrative about working cross-functionally, detailing the roles of team members and how your collaboration led to the successful deployment of an AI project. Discuss the importance of clear communication and alignment within the team.

Here, narrate a specific challenge you faced in an AI project, outlining the steps you took to navigate obstacles. Highlight quantifiable results that demonstrate the impact of your solution and the lessons learned from that experience.

Discuss the cloud platforms you’ve utilized, such as AWS, GCP, or Azure. Highlight particular services or tools from these platforms that enhanced your AI projects, focusing on aspects like scalability, storage, or computing power.

Detail the types of neural networks you've trained, the libraries you used, and the datasets. Explain the approach you took in terms of model selection, fine-tuning, and validation, and how you determined the best performing model.

Explain the importance of maintaining detailed documentation and version control in your projects. Discuss any frameworks or tools you use to track changes and maintain reproducibility, emphasizing their role in fostering collaboration.

Mention soft skills such as strong problem-solving abilities, excellent communication, and project planning skills. Highlight how these skills enable you to effectively collaborate with team members, manage client expectations, and contribute productively to group efforts.