Senior AI Systems Engineer

Essential Functions:

• Lead the deployment, integration, and operational support of AI platforms, tools, and services, ensuring compatibility with existing systems and enterprise processes.

• Design, implement, monitor, and optimize AI infrastructure, working with server, cloud, and platform engineering teams.

• Operationalize machine learning workflows and support AI-enabled applications from development through production deployment and sustainment.

• Build and maintain CI/CD and MLOps pipelines for model packaging, testing, deployment, rollback, and lifecycle management.

• Implement infrastructure automation using scripting, Infrastructure as Code, and configuration management practices.

• Provide ongoing technical support, troubleshooting, root cause analysis, and documentation for AI platforms and user-facing AI services.

• Maintain observability across AI systems through logging, metrics, performance monitoring, alerting, and incident response practices.

• Ensure security, compliance, and governance requirements are met, including participation in audits, vulnerability management, and secure architecture reviews.

• Assess and implement system enhancements to improve performance, scalability, reliability, and cost efficiency.

• Collaborate across divisions to support diverse AI initiatives and align technical implementations with mission and business objectives.

• Evaluate emerging AI tools, frameworks, and infrastructure approaches for operational fit, supportability, and long-term value.

• Develop and maintain technical documentation, runbooks, architecture diagrams, and operational procedures.

Experience and Skills Required:

• Bachelor’s degree in computer science, Engineering, Information Technology, or a related STEM field with 8-10 years of engineering experience.

• 2+ years of experience supporting AI/ML platforms, MLOps workflows, model deployment, or AI-enabled infrastructure.

• Strong coding and automation skills in Python, Bash, or similar scripting languages.

• Experience with AI/ML frameworks and tooling such as PyTorch, Hugging Face, or similar ecosystems.

• Proficiency with DevOps and MLOps practices, including CI/CD pipelines, Git-based workflows, containerization, and Kubernetes.

• Experience deploying AI/ML models or AI services into operational environments, including containerized, cloud, or high-performance computing environments.

• Familiarity with security frameworks and compliance standards such as NIST and CMMC.