What are the responsibilities and job description for the Senior AI Engineer position at The Staff Pad?
Job Description
Lead the future of healthcare AI at St. Peter's Health! As our Lead AI Engineer, you'll serve as the principal technical leader responsible for designing, building, and scaling enterprise AI solutions that improve clinical and business operations. You'll partner closely with the Director of AI to establish the organization's AI platform, architecture, engineering standards, and best practices while mentoring a growing engineering team. This hands-on leadership role combines software architecture, cloud-native engineering, MLOps/LLMOps, and applied AI—including generative AI, machine learning, NLP, and intelligent agents—to deliver secure, reliable, and impactful production solutions across the health system. If you're passionate about building enterprise AI from the ground up and driving innovation in healthcare, this is an opportunity to make a lasting impact.
- Ten or more years of progressive professional experience in software engineering, application architecture, platform engineering, distributed systems, or related technology work, including at least three years of hands-on experience deploying and operating production AI/ML systems.
- Qualifying production AI experience may include traditional machine learning, natural language processing, computer vision, recommender systems, predictive modeling, generative AI, or related applied AI systems.
- Advanced software engineering and architecture proficiency in Python and strong working proficiency in one or more additional enterprise programming languages such as C#, Java, TypeScript, or Go.
- Demonstrated experience architecting and operating distributed, cloud-native applications, APIs, data services, containers, automated delivery pipelines, infrastructure automation, and production observability.
- Deep working knowledge of the production AI/ML lifecycle, including model and service integration, retrieval, agents, evaluation, MLOps or LLMOps, monitoring, reliability, governance, security, and cost optimization.
- Demonstrated ability to lead architecture decisions, mentor senior engineers, coordinate complex technical workstreams, communicate with executives and stakeholders, and guide production outcomes across teams.
Preferred:
- Experience in a healthcare provider, payer, health system, life sciences, financial services, or other highly regulated environment.
- Experience integrating with Epic or another electronic health record, FHIR, HL7, clinical data, medical terminology, or healthcare operational systems.
- Experience establishing enterprise AI or ML platform architecture on Microsoft Azure, AWS, Google Cloud, or a comparable large-scale cloud environment.
- Experience leading MLOps or LLMOps, retrieval-augmented generation, semantic or vector search, agentic systems, model evaluation, and AI application observability at production scale.
- Strong working knowledge of HIPAA, HITECH, healthcare privacy, information security, data governance, responsible AI, clinical safety, and audit requirements.
- Experience evaluating vendors, leading build-versus-buy decisions, defining reference architectures, and establishing engineering standards for a growing technical organization.
A master's degree in a related field is preferred. Advanced education may substitute for a portion of the required experience when accompanied by demonstrated enterprise software architecture, production engineering, and AI deployment capability.