Senior AI Engineer

Blue Cross Blue Shield of Minnesota•Eagan, MN

3d•Remote

About The Position

Blue Cross Blue Shield of Minnesota is hiring a Senior AI Engineer to help design, deploy, and operationalize AI and machine learning solutions that improve data science productivity and reduce operational overhead. In this role, you will work hands on with data scientists, analytics teams, and business partners to translate models and analytical workflows into reliable, scalable, production ready systems. You will focus on automating data pipelines and AI/ML workflows, strengthening cloud based deployment patterns, and improving the efficiency, reliability, and quality of AI solutions in production. You will bring strong execution, collaboration, and technical depth to your work while operating within established architectural standards and partnering closely with senior technical leaders. The ideal candidate is a hands-on Senior level AI Engineer with experience building, deploying, and supporting ML models in production environments. You bring 3+ years of hands on experience with generative AI, large language models, NLP, or related technologies, along with strong proficiency in Python or similar languages and modern ML frameworks such as PyTorch or TensorFlow. You have experience deploying AI solutions in cloud environments such as AWS, Azure, or GCP using established MLOps , CI/CD, containerization, and monitoring practices. You approach AI systems with a strong focus on reliability, scalability, and integration with existing platforms while adhering to security, governance, and quality standards. Strong communication skills, cross functional collaboration, and an interest in mentoring or supporting other engineers are important, with experience in regulated environments such as healthcare preferred.

Requirements

5+ years of related professional experience. All relevant experience including work, education, transferable skills, and military experience will be considered.
3+ years of experience as a data engineer or similar role.
Experience with ML engineering or data science , data analytics, software engineering.
Proficient programming skills in languages like Python, Scala, etc.
Proficient experience with Machine Learning model deployment in production.
Proficient experience with cloud platforms like AWS, Azure, etc.
Proficiency in data pipeline orchestration tools such as Apache Airflow, Docker, Kubernetes, etc.
Proficient experience with SQL and cloud databases like Redshift, Athena, AWS services like Glue, etc.
Proficient experience implementing, testing, and fielding automation for AI and ML solutions.
Excellent communication, collaboration, and presentation skills.
High school diploma (or equivalency) and legal authorization to work in the U.S.

Nice To Haves

Bachelor's or master’s degree in computer science, data Science, or a related field.
Experience with developing and deploying Large Language Models, Retrieval Augmented Generation (RAG) , Generative AI technologies.
Experience with ML frameworks, including Keras , Pandas, TensorFlow, Pytorch , etc.
Proficient experience using AWS technologies for Machine Learning – SageMaker, S3, etc.
Experience with software engineering concepts, including version control and unit testing.
Experience using visualization tools such as PowerBI , Tableau, Looker, etc.
Certification in relevant technologies.
Experience in regulated environments such as healthcare preferred.

Responsibilities

Deploy artificial intelligence (AI) and machine learning (ML) models in production, working with data scientists and other stakeholders in the organization.
Work with a team of data scientists and AI engineers to ensure seamless AI development, integration and deployment in production.
Collaborate with cross-functional teams to understand the analytical and ML needs and provide technical solutions.
Mentor and help junior data scientists to train and run models in cloud environment like AWS and Azure and automation of data science processes.
Develop and maintain unit tests, integration tests, and end-to-end tests for APIs and ML models.
Evaluate, implement emerging tools and cloud services for analytics data engineering to improve productivity of the team.
Work with a multidisciplinary team to build high quality data pipelines that will be used by data scientists and business analysts to generate insights