AI & ML Ops Engineer
ebenezerMinneapolis (MN)
About the role
Overview:
In this role, you design, build, and maintain the engineering platform to deploy and govern AI/ML solutions at scale. You work closely with data scientists, data engineers, and enterprise teams to ensure reliable production services under privacy and security standards. You stay hands-on with deployment, monitoring, and troubleshooting while enabling end-to-end model lifecycle management. You will contribute to production AI applications, agents, and integrations that advance our AI & Automation initiatives. This is a opportunity to shape scalable, observable, and compliant AI systems in a collaborative environment. Compensation / Benefits medical, dental, vision plans life insurance short-term and long-term disability PTO and Sick and Safe Time tuition reimbursement retirement plan
Responsibilities:
Deploy batch and real-time models and related AI services using CI/CD, containers, APIs, and workflow patterns Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services Implement ML lifecycle capabilities (feature stores, model registries, experiment tracking) with enterprise platform owners Govern deployed models with lifecycle controls, lineage, audit trails, and release approvals Implement logging, metrics, monitoring, and alerting for model and service health and performance Integrate data pipelines and APIs with enterprise teams and ensure secure, managed data flows Ensure production solutions have documented dependencies, escalation paths, and 24/7 support where needed Operate LLM, retrieval-based, and agent applications with quality checks and cost monitoring Apply security and IaC standards and collaborate on deployment practices across teams Review code, tests, and documentation to improve reliability and efficiency
Key requirements:
Bachelor in Computer Science, Engineering, Information Systems, or related field or equivalent experience At least three years in software engineering, DevOps, or MLOps with production systems Proficiency in Python or other modern languages (e.g., Java, Go) Hands-on experience deploying and operating batch and/or real-time ML models in production Experience with CI/CD tools (GitHub Actions, GitLab CI, Jenkins, Azure DevOps) Hands-on experience with containers and orchestration on enterprise platforms Experience in cloud/hybrid environments (AWS, Azure, or GCP) Working knowledge of SQL and data-workflow orchestration; integrating model services with pipelines and APIs Experience logging, metrics, monitoring, alerts, and incident investigation across dependencies Understanding of security best practices, RBAC, secrets management, and handling sensitive data Ability to work independently and communicate tradeoffs with cross-functional teams collaboration with cross-functional teams clear communication problem-solving and analytical thinking Python (or Java/Go) CI/CD tooling (GitHub Actions, GitLab CI, Jenkins, Azure DevOps) Containerization and orchestration (Docker, Kubernetes)
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