Engineering Manager, AI

Premera is committed to being a workplace where people feel empowered to grow, innovate, and lead with purpose. By investing in our employees and fostering a culture of collaboration and continuous development, we’re able to better serve our customers. It’s this commitment that has earned us recognition as one of the best companies to work for.

As the
** AI Engineering Manager**, you will develop and lead a team of AI Engineers, Software Engineers, Machine Learning and Data Scientists dedicated to revolutionizing healthcare through cutting-edge
AI/ML solutions and insights. This management role focuses on hands-on ideation, development, deployment, and monitoring of AI/ML models and insights.
* Bachelor’s Degree in Computer Science, Statistics, Mathematics, or a related quantitative field; or 5+ years equivalent professional experience.
* 2+ years of experience leading highly analytical and scientific teams, guiding them through data science or machine learning.
* 5+ years of hands-on experience developing and validating AI/ML solutions in industries with a track record of effectively transitioning models from research to pilot or POC.
* 5+ years of experience conducting quantitative analytics.
* Advanced degree (Masters/PhD) in a quantitative, computational, or scientific field, with demonstrated expertise in AI/ML development methods.
* 2+ years managing cross-functional research collaborations or academic/industry partnerships with a strong focus on innovative AI/ML development.
* 5+ years expertise with core data science libraries (Num Py, Pandas, Matplotlib and scikit-learn) and familiarity with advanced model interpretability libraries (e.g., SHAP, LIME) and statistical tools (e.g., Stats models, PyMC3).
* Experience with NLP libraries (e.g., Hugging Face Transformers, Spa Cy) or specialized packages (e.g., XGBoost, Light

GBM).
* 5+ years experience product ionizing and monitoring AI models and solutions with MLOps frameworks (e.g., MLflow, Kubeflow) for experiment tracking, model packaging, and versioning. Experience applying drift detection and continuous evaluation strategies to maintain the scientific and business relevance of AI/ML solutions.
* 5+ years working within Agile-like test and learning environments, using iterative or sprint-based approaches to develop AI/ML proofs of concept and production-ready deliverables.
* 5+ years of healthcare and healthcare data experience resulting in the creation of solutions and systems for the healthcare industry.
* 2+ years implementing responsible AI practices, including model interpretability, fairness audits, and ethical risk assessments.
* Experience in developing and experimenting with deep learning architectures (CNNs, transformers, LSTMs, etc.) with frameworks such as Tensor Flow, PyTorch, and Keras.
* Proven experience at building and implementing processes, controls, and methods to support the debugging of ML/AI models and enhancing predictive performance.
* Proven experience working with ML life cycles in experimentation contexts, including hypothesis driven development practices, rapid prototyping, and iterative model refinement.
* Experience applying product development lifecycle methodologies to data centric products, aligning roadmaps with market or stakeholder needs and iterating on features through user feedback and data insights.
* Prior exposure to software design patterns, microservices, distributed computing, container orchestration, and other relevant architectures.
* Proficient in SQL for data exploration and feature engineering, with an ability to handle structured and unstructured data in various research contexts. Comfort working with evolving data formats and tools, leveraging data management best practices to support experimentation and prototyping.
* Proven experience with a variety of statistical analyses (e.g., hypothesis testing, experimental design, regression analysis, clustering, time series analysis, anomaly detection, and sequence analysis) to data-driven decision making.
* In-depth experience developing supervised predictive models (e.g., logistic regression, random forests, gradient boosting) and applying unsupervised…