AI & ML Ops Engineer

Fairview Health Services•Minneapolis, MN

About The Position

The AI & ML Operations Engineer designs, builds, and maintains engineering processes and platform capabilities for deploying, monitoring, governing, and maintaining AI/ML solutions at scale. The role converts validated analytical work into reliable production solutions and manages the operational lifecycle of models and related services in accordance with enterprise privacy, security, and support standards. The engineer partners with data scientists, data engineers, cloud and infrastructure teams, API and Interoperability teams, Cybersecurity, service owners, and other engineering teams. The role is hands-on in engineering, testing, deployment, and troubleshooting, while using enterprise-managed capabilities for production data movement, integration, and infrastructure. Scientific-method validation remains with the Data Scientist and designated reviewers; this position implements operational controls for deployed solutions. As part of a new AI & Automation engineering team, this role directly designs, builds, deploys, and supports production AI applications and services. Work may include agents, retrieval-augmented generation, orchestration, APIs and integrations, automated evaluation and testing frameworks, observability, monitoring, release and rollback processes, and model/application lifecycle management.

Requirements

B.S./B.A. in Computer Science, Engineering, Information Systems, or a related field, or an equivalent combination of education and relevant experience.
At least three years of experience in software engineering, DevOps, or MLOps, including work with production systems.
Proficiency in Python or another modern programming language, such as Java or Go, sufficient to build, test, troubleshoot, and maintain production components.
Hands-on experience deploying and operating batch and/or real-time machine-learning models in production.
Experience with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, or Azure DevOps; source control, code review, automated testing, and controlled releases.
Hands-on experience with containerization and orchestration technologies using enterprise-approved platforms and tools.
Experience in cloud and/or hybrid environments such as AWS, Azure, or GCP.
Working knowledge of SQL and experience with large datasets and data-workflow orchestration; ability to integrate model services with enterprise-managed pipelines and APIs.
Experience implementing logging, metrics, monitoring, alerts, and operational diagnostics and investigating failures across model, application, data, and infrastructure dependencies.
Understanding of security best practices, role-based access control, secrets management, and handling sensitive data.
Ability to independently deliver assigned engineering work, communicate technical tradeoffs, and collaborate with data scientists, clinicians, data/API teams, cloud/DevOps, security, and support functions.

Nice To Haves

Experience in healthcare, health technology, or another regulated industry handling PHI/PII.
Familiarity with EHR/EMR integration and healthcare data standards such as HL7/FHIR, ICD, CPT, LOINC, or SNOMED.
Experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML, including model registries, feature stores, and experiment tracking.
Experience with ETL/ELT, data warehouse or lakehouse platforms, enterprise API platforms, infrastructure as code, and formal production-support practices.
Experience monitoring model drift, fairness, and explainability and implementing healthcare AI governance controls.
Experience operating LLM, retrieval-based, or agent applications, including quality evaluation, tracing, prompt/configuration management, and cost monitoring.

Responsibilities

Deploy batch and real-time models and related AI services using approved CI/CD, containers, APIs, and workflow patterns; build and maintain assigned deployment and runtime components and coordinate dependencies with enterprise platform owners.
Automate packaging, versioning, testing, promotion, rollback, and recovery for models and services; implement retraining or recalibration workflows using criteria defined with data scientists and required validation and release approvals.
Implement, integrate, configure, and operate approved feature stores, model registries, experiment tracking, and related ML lifecycle capabilities with enterprise platform owners; partner with Cloud & AI Deployment Engineering and central cloud teams on compute, storage, networking, and deployment.
Implement approved lifecycle controls, access restrictions, lineage, audit trails, change records, and release approvals for deployed models; maintain traceability across data, code, model, and configuration versions.
Implement logging, alerts, and monitoring for model performance, drift, data quality, latency, availability, and errors; collaborate on measures, thresholds, incident response, and service objectives.
Coordinate with central data-engineering teams on managed data pipelines and with enterprise API and Interoperability teams on real-time and application integrations, including MuleSoft or other approved API capabilities; coordinate monitoring, recovery checkpoints, restart procedures, security, and support ownership.
Ensure production solutions have documented dependencies, operational procedures, escalation paths, and ongoing ownership before release; arrange support with responsible enterprise teams, including 24/7 coverage where required for critical services.
Operate assigned LLM, retrieval-based, and agent applications using automated quality checks, model/tool tracing, prompt and configuration versioning, and usage/cost monitoring within the common production lifecycle.
Apply enterprise privacy, security, encryption, role-based access, and sensitive-data controls; follow common infrastructure-as-code, pull-request, and deployment practices with responsible teams and bring needed tooling or design exceptions through established review processes.
Help scientists and partner teams move prototypes into supported production solutions; review code, tests, and documentation, resolve incidents across dependencies, provide constructive technical feedback, and improve reliability, reproducibility, performance, and efficiency.