Architect/ Principal AI Engineer

As a Principal AI Engineer at our company, your key responsibilities include defining and executing the AI strategy for next-generation lab operations. You will drive the adoption of Generative AI and deep learning to optimize workflows while leading research and development in areas like Retrieval-Augmented Generation (RAG) and prompt engineering.

To qualify for the Principal AI Engineer role at our company, you should have at least eight years of software development experience with a strong focus on AI/ML. Deep expertise in Python and frameworks like PyTorch and TensorFlow is essential, along with proven experience deploying machine learning models in production.

As a Principal AI Engineer, your work will revolutionize laboratory operations by implementing AI-powered solutions that enhance efficiency and decision-making. You'll be guiding lab personnel through complex tasks with intelligent automation methods and optimizing workflows to enable smarter scientific discoveries.

The Principal AI Engineer at our company will primarily utilize AWS Bedrock, SageMaker, PyTorch, TensorFlow, and Scikit-learn to architect and scale AI systems. Knowledge of MLOps and cloud infrastructure best practices is also critical for this position.

While preferred, having experience in healthcare and life sciences regulatory requirements, like HIPAA and GxP, is advantageous for a Principal AI Engineer. This background helps ensure that AI solutions are designed with security and compliance in mind, especially in regulated environments.

In your response, highlight specific projects where you've utilized Generative AI, explaining the techniques and outcomes. Discuss how you envision applying those skills to our company’s lab operations, focusing on enhancing workflows and decision-making.

RAG is a significant technique that merges traditional information retrieval with generative models. In your answer, emphasize its importance in generating contextually relevant responses in lab environments, enhancing accuracy in AI-driven automation.

When responding, focus on the lifecycle of machine learning model deployment. Discuss your experience in automating model training, validation, and deployment using industry best practices to ensure scalability and reliability.

Mention your approach to fostering cooperation among technical and non-technical stakeholders. Share examples of successful projects where you led cross-functional teams to deliver innovative AI solutions.

Share a specific instance that highlights your problem-solving skills and technical expertise. Discuss the challenges faced, the innovative solutions implemented, and the outcomes that positively impacted the project.

In your answer, explain your understanding of ethical considerations in AI, alongside relevant regulations. Highlight your experience designing AI systems that incorporate compliance mechanisms, especially in the context of healthcare and life sciences.

Elaborate on your experience with cloud platforms like AWS. Discuss how leveraging cloud infrastructure helps in building scalable, efficient AI systems and ensure reliable access to necessary resources.

Explain your methodologies for managing tasks and projects effectively, such as using agile principles or specific tools that enhance visibility and collaboration across teams to keep projects on track.

Discuss how intuitive front-end design enhances user engagement and usability of AI applications. Provide examples from your experience where strong UI design significantly improved user adoption of AI solutions.

Share your insights on emerging AI trends and technologies, and discuss how these innovations could redefine laboratory automation processes. Emphasize any research or projects that align with these trends and their potential implementations.