AI/ML Ops Engineer

Introduction

Maryland Benefits (MD Benefits) is a dynamic, cloud-based platform. This enterprise-wide digital service allows organizations to build, test, host, operate, and integrate mission-driven applications, data, and emerging technologies. MD Benefits offers cloud-based Platform-as-a-Service (PaaS) capabilities, a shared data architecture, and product development services, all developed by the State of Maryland to help multiple agencies deliver and manage health, human, and social service benefits and programs. On July 1, 2025, the operation of the MD Benefits shared platform and statewide applications transitioned from the Department of Human Services (DHS) to the Department of Information Technology (DoIT).

• This is a contractual position with limited benefits*

Main Purpose

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. Develops, maintains, and optimizes the software development environment. Responsible for infrastructure, build, integration, and software deployment processes. This specialized role bridges the gap between data science and platform operations, ensuring that 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.

Position Duties

• Design and automate reproducible ML pipelines using various tools for ML pipelines.
• Automate training, tuning, evaluation, and deployment of ML models to production endpoints.
• Manage model versioning, drift detection, and performance monitoring using ML model management and monitoring tools.
• Integrate and operationalize Generative AI models for text, image, and multi-modal use cases.
• Build and deploy Conversational AI solutions and integrate with enterprise apps.
• Automate intelligent document processing, integrating OCR with downstream ML models.
• Use data management platforms to manage, catalog, and govern ML datasets and assets across the organization.
• Enforce secure and governed access to training data, features, and model outputs through tagging, access policies, and data domains.
• Implement CI/CD pipelines for ML using various CI/CD tools.
• Automate infrastructure provisioning for ML experiments using Infrastructure as Code (IaC) tools.
• Ensure reproducibility, scalability, and cost optimization of ML infrastructure (e.g., GPU instances, endpoints).
• Build monitoring dashboards and alerts for model performance, latency, cost, and failures.
• Integrate various monitoring and explainability tools for bias, explainability, and drift detection.
• Collaborate with data scientists to productionize notebooks and turn prototypes into resilient services.

Minimum Qualifications

Education:

• Bachelor's degree in Computer Science, Information Systems, or a related field; an advanced degree is preferred.

General Experience:

• At least eight (8) years of relevant experience.
• 5+ years of experience in Data Engineering, DevOps, or Cloud Infrastructure roles.
• Hands-on experience with various cloud data services.
• Expertise in scripting and automation using Python, Bash, or Shell.
• Proficiency in IaC tools like Terraform, CloudFormation, or CDK.
• Experience with Git, CI/CD pipelines, and monitoring tools.

Preferred Qualifications:

• Technical background and experience in data and AI/ML projects.
• Experience with Apache Spark
• Familiarity with data lake architecture, data mesh, and data governance frameworks.
• Exposure to event-driven architecture, serverless, or streaming data processing (e.g., Kinesis, Kafka).