Junior AI Engineer

As a Junior AI Engineer at Maneva, you'll be focused on training vision models like classification, object detection, and segmentation while also assisting with MLOps workflows. This role will involve collaboration on deploying AI/ML models and monitoring their performance, making it an exciting opportunity to learn and grow in the AI field.

To be considered for the Junior AI Engineer role at Maneva, candidates should have a Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field. Familiarity with computer vision, Python, and basic AI/ML libraries is crucial. Relevant coursework or certifications in MLOps are also a plus.

Yes, Maneva has a preference for candidates located in tech-leading cities such as Toronto, Montreal, or San Francisco. This helps in fostering a collaborative and dynamic team environment, although candidates from other locations are also welcome to apply.

At Maneva, a successful Junior AI Engineer must have strong problem-solving skills, attention to detail, and excellent communication abilities. A proactive learning attitude and the ability to adapt in a fast-paced environment are equally important to thrive in this role.

While the Junior AI Engineer role at Maneva is entry-level, familiarity with computer vision or machine learning through academic projects or internships is beneficial. Experience within industrial or manufacturing environments is a bonus, helping candidates better understand the challenges they will address.

As a Junior AI Engineer, you will work on developing and training AI models for manufacturing applications, optimizing production processes, and assisting with MLOps workflows. These projects will focus on real-world challenges faced in the manufacturing sector, allowing you to make a tangible impact.

Maneva fosters a supportive environment for professional growth. As a Junior AI Engineer, you will have opportunities for mentorship, collaborative projects, and the chance to develop your skills with cutting-edge AI technologies, with clear pathways for career advancement.

When answering about your experience with computer vision or machine learning, highlight specific projects, coursework, or internships that involved these technologies. Be sure to mention the tools you used, such as Python libraries, and any practical applications, like building models or analyzing datasets.

In your response, outline the steps you would take to train a vision-based AI model. Start with data collection and preprocessing, then explain how you would select a suitable architecture, configure parameters, train the model, and evaluate its performance, emphasizing the importance of iterative training cycles.

Discuss the potential challenges in AI deployment, such as data quality, model maintenance, and ensuring real-time performance. Highlight strategies to mitigate these challenges, like implementing robust monitoring systems and employing MLOps practices to streamline workflows.

Share how you actively keep abreast of AI advancements through academic journals, online courses, tech blogs, and community forums. Mention any specific resources or networks you follow to enhance your knowledge and skills regularly.

Highlight your collaborative experiences by mentioning specific projects where you worked as part of a team. Emphasize your communication skills, how you contributed to discussions, handled disagreements, and the importance of teamwork in achieving project goals.

Explain that monitoring deployed AI models involves tracking performance metrics, user feedback, and error rates. Stress the importance of proactive monitoring to quickly identify drifts or degradation in performance and being prepared to retrain models as necessary.

Discuss a systematic approach to debugging AI models, which includes reviewing logs, checking data input/output, and analyzing model predictions to identify inconsistencies. Emphasize the importance of methodical troubleshooting and documenting findings.

Discuss any exposure you have had to MLOps tools such as TensorFlow Extended (TFX), Kubeflow, or MLflow. Explain how you’ve used these tools in previous projects to streamline model deployment or monitoring processes, demonstrating practical knowledge of their benefits.

Share your enthusiasm for joining Maneva by focusing on the innovative AI solutions they provide within the manufacturing sector. Explain how their mission aligns with your career goals and what you hope to contribute to their projects.

Provide your insights on the future of AI in manufacturing, discussing trends like increased automation, predictive analytics, and the role of AI in decision-making processes. Highlight the potential for AI to drive efficiency, safety, and innovation in production environments.