Principal AI Engineer

Blue Cross Blue Shield of Minnesota•Eagan, MN

2d•Hybrid

About The Position

At Blue Cross and Blue Shield of Minnesota, we are committed to paving the way for everyone to achieve their healthiest life. We are looking for dedicated and motivated individuals who share our vision of transforming healthcare. As a Blue Cross associate, you are joining a culture that is built on values of succeeding together, finding a better way, and doing the right thing. If you are ready to make a difference, join us. The Impact You Will Have Blue Cross Blue Shield of Minnesota is hiring a Principal AI Engineer. In this role, you will serve as a technical lead driving the development, deployment, and evolution of enterprise‑grade AI and machine learning solutions. You will partner closely with data science, engineering, cloud, product, and business teams to deliver scalable, production‑ready AI capabilities that solve complex business problems, streamline operations, and elevate customer and clinician experiences. This role requires deep hands‑on engineering expertise, and the ability to guide the full lifecycle of AI solutions—from concept through operationalization. The ideal candidate is a hands‑on Principal‑level AI Engineer with extensive experience building, deploying, and operating enterprise‑grade AI and machine learning solutions in production. They bring deep expertise in generative AI, large language models, NLP, and multimodal systems, along with strong proficiency in Python or similar languages and modern ML frameworks such as PyTorch or TensorFlow. This individual has a proven ability to architect and operationalize AI solutions in cloud environments like AWS, Azure, or GCP using modern MLOps, CI/CD, containerization, and monitoring practices. They think holistically about AI systems, ensuring seamless integration with enterprise platforms while maintaining high standards for security, governance, and quality. Strong communication skills, cross‑functional collaboration, and a passion for mentoring other engineers are essential, with experience in regulated environments such as healthcare strongly preferred.

Requirements

8+ years of professional experience in data engineering, machine learning engineering, software engineering, or related fields. All relevant experience including work, education, transferable skills, and military experience will be considered.
5+ years of experience deploying and maintaining ML/AI models in production at enterprise scale.
Advanced proficiency in Python, Scala, or comparable languages.
Expertise with cloud architectures (AWS, Azure, GCP), including ML‑focused services (e.g. SageMaker, AzureML).
Strong experience with orchestration and containerization tools such as Kubernetes, Docker, Airflow, etc.
Deep understanding of machine learning algorithms, deep learning frameworks (PyTorch, TensorFlow), and NLP technologies.
Demonstrated experience with generative AI, multimodal systems, LLM fine‑tuning, and prompt engineering.
Strong SQL and experience with cloud‑native data ecosystems.
Proven ability to architect, deploy, and monitor complex AI/ML solutions in production.
Excellent communication, collaboration, and technical leadership skills.
High school diploma (or equivalency) and legal authorization to work in the U.S.

Nice To Haves

Advanced degree in Computer Science, AI, Data Science, Engineering, or a related field.
Experience in healthcare, health services, or insurance.
Expertise in RAG pipelines, vector databases, and enterprise knowledge systems.
Experience with real‑time streaming systems, edge AI, or high‑performance model serving.
Background in full‑stack engineering incorporating AI‑driven user experiences.
Professional certifications in cloud architectures or ML/AI technologies.

Responsibilities

Advanced Solution Development Build, deploy, and optimize LLM‑based, multimodal, and predictive AI models.
Develop intelligent automation to streamline workflows and reduce operational friction.
Implement NLP, conversational AI, and real-time generative systems across modalities.
AI System Integration & Full‑Stack Engineering Oversee AI system integration with enterprise platforms, cloud services, APIs, and data pipelines.
Architect and maintain production‑grade ML infrastructure, including CI/CD and monitoring.
Operationalize AI/ML models in cloud environments such as AWS, Azure, or GCP.
Governance, Quality & Compliance Ensure AI solutions meet regulatory and internal governance requirements.
Implement comprehensive testing frameworks across all system layers.
Collaboration & Mentorship Partner with cross‑functional teams to deliver robust, production‑ready AI solutions.
Mentor engineers in AI architecture, cloud engineering, and advanced model integration.
Provide technical mentorship in prompt engineering, fine‑tuning, and responsible AI practices.