Senior AI/ML DevOps Engineer

Hours: Monday-Friday Days

JOB SUMMARY

The Senior AI / ML DevOps Engineer designs, develops, deploys, and supports scalable machine learning solutions that enable advanced analytics and AI capabilities across HonorHealth. This role operationalizes models and pipelines, monitors performance, and partners with data, engineering, and stakeholders to deliver reliable solutions aligned to governance and data handling expectations. The role partners closely with data engineers, data scientists, platform teams, and business stakeholders to translate complex needs into production-ready deployments, monitor ongoing performance, and continuously improve AI/ML capabilities aligned to governance and operational standards.

ESSENTIAL FUNCTIONS

• Leads the design and implementation of the environment that deploys scalable AI/ML solutions, including predictive models, large language model use cases, and agentic workflows that support clinical, operational, and business objectives.
• Develops and maintains end-to-end machine learning and RAG pipelines supporting data ingestion, application feature engineering, training, evaluation, deployment, and lifecycle management.
• Architects and supports production-grade DevOps practices, including CI/CD, model versioning, automated testing, monitoring, alerting, retraining, and rollback strategies.
• Deploys and manages AI/ML applications in cloud environments, ensuring solutions are secure, reliable, performant, and operationally supportable.
• Implements observability and performance evaluation practices to manage compute resource utilization, performance, and cost efficiency. Works with AI development team to a continuous improvement feedback loop.
• Ensures AI/ML and agentic solutions comply with data governance, privacy, security, and responsible AI expectations, including HIPAA-aligned practices where applicable.
• Creates and maintains technical documentation for architectures, models, workflows, operational procedures, assumptions, limitations, and support processes.
• Troubleshoots complex pipeline, infrastructure, model, and integration issues; implements fixes and drives continuous improvement in operational stability and delivery efficiency.
• Core Skills: Hands-on experience with cloud-native AI/ML services and infrastructure, preferably in Google Cloud Platform (GCP), including services for training, inference, orchestration, and scalable compute
• Proficiency info MLOps, DevOps, and software engineering practices such as CI/CD, Git-based workflows, containerization, Infrastructure as Code, and automated testing
• Proficiency with establishing connections via API calls and MCP servers.
• Strong SQL and data engineering skills with experience in large-scale data processing, backend engineering, and integration across enterprise data platforms
• Experience with API development and systems integration to embed AI/ML capabilities into enterprise applications and workflows
• Strong analytical and problem-solving skills with the ability to diagnose issues across data, models, infrastructure, and orchestration layers
• Excellent communication and collaboration skills with the ability to document and explain technical concepts clearly to both technical and non-technical stakeholders
• Performs other duties as assigned.