AI Engineer

As an AI Engineer at Reach Industries, your main responsibilities will include developing and implementing advanced AI models focused on visual data analysis. You'll work closely with our cross-functional teams to understand project goals and design optimal AI solutions. Additionally, you will preprocess and analyze visual data, train and optimize AI models using various frameworks, and ensure seamless integration of AI functionalities into existing systems. Your contributions will directly impact the development of our software platform, Lumi, enhancing the tools used by scientists globally.

To be considered for the AI Engineer position at Reach Industries, you should have a Bachelor's degree or higher in fields such as Computer Science, AI, Engineering, or Mathematics. In addition, a minimum of 3 years of commercial experience in similar roles is required. Proficiency in programming languages like Python and C++ is essential, alongside experience with AI libraries such as TensorFlow, PyTorch, and OpenCV. Lastly, strong analytical skills and experience in Agile/Scrum environments will be beneficial for your success in this role.

An AI Engineer at Reach Industries must possess strong technical skills, particularly in Python and C++ for AI model development. Proficiency in frameworks like TensorFlow, Keras, PyTorch, and PaddlePaddle for model training and optimization is crucial. Additionally, familiarity with OpenVINO for inference and tools like OpenCV for visual data processing will be vital in your day-to-day work. Knowledge of cloud platforms, such as AWS, and tools for visual data annotation like CVAT will also enhance your ability to contribute effectively to our projects.

The role of an AI Engineer at Reach Industries closely supports scientists by developing AI models that automate and enhance data analysis processes in laboratories. By refining and upgrading our Lumi platform, you’ll be providing scientists with powerful tools that free them to focus on the creative dimensions of their research. This support enables them to tackle complex scientific problems more effectively, allowing for faster discoveries and development in areas such as healthcare and environmental science.

As an AI Engineer at Reach Industries, you will enjoy a competitive salary based on your experience. Additionally, the company offers stock options, a generous holiday policy (including 27 days plus bank holidays), and an 8% pension contribution. You'll also benefit from flexible working hours, access to private healthcare, enhanced maternity leave, and a dedicated budget for your professional growth through training and conferences. We strive to create a supportive and balanced work environment where you can thrive.

In addressing this question, consider highlighting your experience with specific AI frameworks and libraries. Discuss key projects where you developed models using TensorFlow, Keras, or PyTorch, emphasizing the challenges you faced and how you overcame them. Illustrate your familiarity with preprocessing data and optimizing models for performance and accuracy, demonstrating your hands-on expertise in the field.

When answering this question, discuss your systematic approach to problem-solving. Include steps such as defining the problem clearly, exploring available resources, collaborating with team members, and employing iterative testing to refine AI models. Emphasize your analytical skills and your ability to pivot when encountering obstacles, showcasing your resilience and adaptability in complex projects.

Collaboration is fundamental in AI engineering, especially in a multidisciplinary setting like Reach Industries. Describe your experiences working alongside data scientists, software engineers, and domain experts, fostering an environment of open communication and shared knowledge. Explain how collaborative efforts enhance project outcomes and contribute to accelerated problem-solving, demonstrating your commitment to team success.

To effectively answer this question, detail your knowledge in model optimization techniques for both edge devices and cloud environments. Discuss tools like OpenVINO for optimizing inference and strategies for reducing model complexity while maintaining accuracy. Providing examples from past projects where you successfully executed these optimizations will strengthen your response, showcasing your practical experience and knowledge.

Staying current in the rapidly evolving AI field involves a mix of continual learning and engagement. Discuss your routine for following AI research by reading journals, attending webinars, and participating in conferences. Mention contributions to professional communities or online forums where you exchange ideas and best practices with peers, demonstrating your dedication to professional growth and ongoing education.

Choose a project that highlights your skills as an AI Engineer. Provide an overview of the project's objectives, your contributions, tools and frameworks used, and the ultimate impact. Be sure to include any metrics or data demonstrating the project's success, as this showcases your ability to drive significant outcomes and reveals your real-world experience.

In your answer, outline your steps for data preprocessing, including data collection, cleaning, and augmentation. Discuss specific libraries you use, like OpenCV and scikit-learn, detailing how you extract meaningful insights from raw visual data. Providing insights or challenges faced during preprocessing tasks would illustrate your approach and understanding of the importance of this foundational step in AI projects.

When designing AI solutions, consider factors like scalability, performance, and user requirements. Describe how you ensure your designs align with business objectives and provide practical benefits to end users. Including a method to gather feedback and iteratively improve AI models can further illustrate a user-centric approach to your design process.

Discuss your process for integrating AI models, which may include assessing system requirements, preparing data pipelines, and ensuring seamless functionality. Talk about your collaboration with other team members, emphasizing the importance of communication and testing to achieve a successful integration that enhances system capabilities and meets user needs.

When responding, share a specific example of a challenging AI problem, focusing on the approach you took to address it. Discuss the thought process behind your strategies and any frameworks or tools you employed. This response should reveal your critical thinking skills, perseverance, and ability to innovate when facing tough situations.