Artificial Intelligence Operations Engineer (Full-time Hybrid, Morrisville, NC Based)

The AI Operations Engineer is responsible for the operational stability, reliability, and continuous performance of deployed AI models and services. The AI Operations Engineer ensures that AI solutions built are seamlessly operationalized, monitored, and maintained in production environments. By managing the lifecycle of AI applications, from automated deployment and infrastructure scaling to real-time monitoring and incident response, this role ensures that AI-driven capabilities remain secure, cost-efficient, and highly available to support Alliance Health’s mission-critical clinical and operational workflows.

AI Model Deployment & Infrastructure Operations

• →Deploy, monitor, support, and maintain AI models in production environments, ensuring seamless integration with existing enterprise systems
• →Develop and manage automated CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and full lifecycle management
• →Architect and manage hybrid cloud-AI environments that support multi-model, multi-agent, and edge AI deployments
• →Implement infrastructure automation using scripting, Infrastructure as Code (IaC), and configuration management to ensure scalable and resilient AI services
• →Design and optimize AI infrastructure in collaboration with server, cloud, and platform engineering teams to support high-performance workloads

AI System Observability & Reliability

• →Maintain comprehensive observability across all AI systems through advanced logging, metrics, performance monitoring, tracing, and alerting 
• →Detect and respond to critical operational risks, including model drift, performance degradation, and training-serving skew 
• →Define and monitor key operational metrics such as availability, latency, throughput, token consumption, model cost, and task-completion rates 
• →Provide ongoing technical support, including rapid incident response, troubleshooting, and root cause analysis for AI platforms and user-facing services
• →Improve platform reliability, resilience, and cost-efficiency through continuous tuning, automation, and operational improvements

AI Governance, Security & Compliance

• →Partner with Cybersecurity, Risk, and AI Governance teams to implement and evidence required security controls
• →Implement threat-resistant AI infrastructure and enforce security frameworks to protect against data exfiltration and unauthorized access 
• →Ensure compliance with regulatory obligations and internal policies by maintaining model version control, audit trails, and lineage/traceability for data, prompts, and outputs
• →Enforce data governance patterns, including privacy controls, data masking, and the secure management of secrets and identity across AI workflows

Operational Enablement & Collaboration

• →Enable self-service platform consumption for AI Engineers and Data Scientists via standardized APIs, reusable pipelines, templates, and documentation
• →Collaborate across divisions to align technical implementations with mission-critical business objectives and reduce operational disruption 
• →Communicate technical constraints such as latency, cost, and performance effectively to business stakeholders and technical leads
• →Evaluate emerging AI tools, frameworks, and infrastructure approaches to determine their operational fit, supportability, and long-term value for the organization 
• →Develop and maintain essential technical documentation, including runbooks, architecture diagrams, and standard operational procedures

User and Technical Support & Troubleshooting

• →Provide hands-on technical assistance to resolve issues arising from the integration of AI models into existing business workflows and systems
• →Perform root cause analysis for technical failures in AI pipelines, including issues related to data integration, model performance, or infrastructure connectivity
• →Support users in navigating technical complexities related to new AI platforms, tools, and orchestration frameworks to ensure minimal disruption to their operations

Education & Experience

Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field from an accredited college or university, and three (3) years of experience developing, implementing, and deploying software, machine learning, and/or artificial intelligence solutions. Experience must include integrating, deploying, monitoring, and maintaining AI/ML models and/or third-party AI/ML tools within production environments for internal business operations and/or customer-facing applications.

At least two (2) years of experience utilizing Python and related machine learning and artificial intelligence libraries and frameworks to develop, test, deploy, and support AI/ML solutions is required.

• MLOps & Model Lifecycle Management: Automating model deployment, testing, versioning, and rollback processes (CI/CD for ML).
• LLMOps & Generative AI Operations: Managing prompt configurations, evaluation pipelines, and lifecycle management for LLMs.
• Infrastructure as Code (IaC) & Automation: Using tools like Terraform or Ansible to deploy and manage cloud-based AI infrastructure.
• Cloud & Hybrid Architecture: Managing compute resources and hybrid cloud environments (Azure) for AI workloads.
• Observability & Monitoring: Implementing logging, metrics, and alerting for model drift, latency, accuracy, and token consumption.
• Containerization & Orchestration: Docker and Kubernetes for scaling and managing AI microservices and agents.
• Programming & Scripting: Python, with familiarity with industry standard AI and ML libraries.
• AI Security & Governance: Implementing threat-resistant infrastructure, IAM, encryption, and compliance within AI workflows.
• Technical Troubleshooting & Support: Ability to perform root cause analysis for complex integration issues and provide high-level support to technical users.
• AI-Augmented Productivity: Ability to utilize AI tools (GitHub Copilot) to accelerate operational tasks and documentation.