- 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 insightsNice To Have:
- 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.Required Skills & Experiences:
- 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.Make A Difference
Blue Cross is an Equal Opportunity and Affirmative Action employer that values diversity. All qualified applicants will receive consideration for employment without regard to, and will not be discriminated against based on race, color, creed, religion, sex, national origin, genetic information, marital status, status with regard to public assistance, disability, age, veteran status, sexual orientation, gender identity, gender expression, or any other legally protected characteristic.
Reasonable Accommodation for Job Seekers with a Disability: If you require reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please direct your inquiries to
All roles require a high school diploma (or equivalency) and legal authorization to work in the U.S.
Blue Cross and Blue Shield of Minnesota and Blue Plus are nonprofit independent licensees of the Blue Cross and Blue Shield Association. -
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Senior AI Engineer - Eagan, United States - Blue Cross and Blue Shield of Minnesota
Description
About Blue CrossBlue Cross and Blue Shield of Minnesota is one of the most recognized and trusted health care brands in the world with 2.5 million members. We're committed to reinventing health care to improve health for our members and the community. We hope you'll join us.
How Is This Role Important To Our Work?
This role collaborates closely with both data science subject matter experts (SMEs) and business SMEs to address and rectify process inefficiencies within the realms of data science and machine learning. Its primary mission involves automating data pipelines and workflows, with a particular focus on enhancing the productivity of data scientists and data analysts while simultaneously reducing organization operational overhead.
A Day In The Life:
