AI/ML Ops Engineer

Minimum Qualifications: Bachelor's degree in Computer Science, Information Technology, or a related field. (Five years of relevant professional experience may be substituted for a degree).5+ years of experience in a technical support or operations role, with a focus on cloud technologies. Proven experience with AWS services, including EC2, S3, VPC, and IAM.Strong understanding of networking concepts, including TCP/IP, DNS, and load balancing. Experience with containerization technologies such as Docker and Kubernetes. Excellent troubleshooting and problem-solving skills. Strong communication and interpersonal skills. Ability to work effectively in a fast-paced, high-pressure environment. AI/ML Ops Engineer Position Description: The AI/ML Ops Engineer is responsible for operationalizing and managing the entire lifecycle of artificial intelligence and machine learning models on the Maryland Benefits Platform. This specialized role bridges data science and platform operations, ensuring ML models are deployed, monitored, and maintained with the same rigor and reliability as traditional software applications. The engineer will build and manage the infrastructure and automation pipelines necessary for robust, scalable, and secure machine learning operations (MLOps), enabling the platform to leverage data-driven insights effectively.

Responsibilities: ML Pipeline Automation: Design, build, and maintain CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for machine learning models. Automate all stages of the ML lifecycle, including data preprocessing, model training, validation, deployment, and monitoring. Model Deployment & Serving: Deploy trained ML models as scalable, high-availability API services in a production environment, leveraging containerization (Docker) and orchestration platforms (Kubernetes/EKS). Manage deployment strategies such as A/B testing, canary, and blue-green deployments. Infrastructure Management: Provision and manage the cloud infrastructure required for both model training and real-time inference, including GPU-accelerated compute instances and scalable data storage solutions on AWS.Model Monitoring & Maintenance: Implement comprehensive monitoring solutions to track model performance, data drift, and concept drift in production. Establish automated alerts and retraining triggers to ensure models remain accurate and reliable over time. Collaboration & Tooling: Work closely with data scientists to understand model requirements and assist in packaging models for production. Collaborate with PaaS and Cloud Engineering teams to integrate MLOps tooling with the existing platform infrastructure. Manage and support the MLOps toolchain, which may include platforms like Kubeflow, MLflow, or AWS SageMaker.