Principal AI Engineer

The Principal AI Engineer at Gainwell Technologies is responsible for designing, building, and deploying advanced AI models, including Generative AI, within healthcare applications. Key responsibilities include developing end-to-end AI solutions, processing large datasets, optimizing model performance, and ensuring compliance with industry regulations such as HIPAA. You'll also collaborate with other engineers to integrate AI models seamlessly into existing technology stacks.

To be considered for the Principal AI Engineer role at Gainwell Technologies, you should hold a Master’s or Ph.D. in Computer Science, AI, or Data Science. Essential qualifications also include a minimum of 5 years of experience in AI/ML engineering, hands-on expertise in various AI frameworks, and a solid understanding of cloud platforms and big data technologies.

Yes, the Principal AI Engineer role at Gainwell Technologies is a fully remote opportunity, allowing you to work from anywhere in the U.S. This flexibility is designed to help you maintain a healthy work-life balance while contributing to important projects within our healthcare technology platform.

Candidates for the Principal AI Engineer position at Gainwell Technologies should possess strong programming skills, particularly in Python, as well as familiarity with machine learning frameworks like TensorFlow and PyTorch. Additionally, experience in developing and optimizing large language models will be highly beneficial.

The deadline to submit applications for the Principal AI Engineer role at Gainwell Technologies is April 1, 2025. Make sure to submit your application before this date to be considered for this exciting opportunity!

When discussing your experience with Generative AI, focus on specific projects where you developed or deployed models. Highlight your proficiency in techniques such as natural language processing or image generation, and illustrate how your work achieved measurable outcomes within healthcare applications.

To ensure model compliance with healthcare regulations, emphasize your knowledge of HIPAA and other relevant guidelines. Discuss specific steps you take to maintain compliance throughout the lifecycle of AI development, including data handling practices and ethical considerations in AI deployment.

For optimizing AI model performance, mention methodologies like hyperparameter tuning, cross-validation, and performance metrics evaluation. Describe how you leverage MLOps principles to iteratively improve model accuracy and efficiency, en route to real-world applications.

When asked to describe an end-to-end AI solution, offer a narrative that includes problem identification, solution design, implementation, and deployment. Highlight any collaboration with cross-functional teams and the impact your solution had on operational efficiency or patient outcomes.

In handling large datasets, discuss your experience with data preprocessing, feature engineering, and utilizing big data technologies like Spark or Hadoop. Emphasize your strategies for ensuring data quality and compliance while effectively managing and analyzing extensive datasets.

When sharing an example of collaboration with software engineers, focus on a specific project where teamwork was crucial. Detail how you communicated your AI needs and findings to facilitate integration with software components, leading to a successfully deployed solution.

To stay current with advancements in AI and ML, mention specific resources you leverage, such as academic journals, conferences, and online courses. Share your commitment to continuous learning and how you apply new insights and technologies to improve your work.

When asked about translating business challenges into AI solutions, demonstrate your analytical skills by describing your approach to identify core issues and define appropriate AI strategies. Emphasize the importance of business acumen in guiding your technical decisions.

In discussing your experience with AI deployment in cloud environments, highlight specific cloud platforms you've worked with, such as AWS, Azure, or GCP. Describe the processes you followed to ensure scalability, reliability, and efficiency in your deployments.

To convey your enthusiasm for working in healthcare technology, articulate how AI can transform patient care and operational efficiency. Share personal motivations, such as a commitment to innovative healthcare solutions and the meaningful impact of your technical work on people's lives.