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.
• Familiarity with AI security functionality in enterprise environments including OAuth
• Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.
  

Preferred:

• Advanced degree or certifications related to AI or machine learning.
• Experience integrating AI models into scientific workflows.
• Familiarity with large language model (LLM) APIs and orchestration frameworks such as OpenAI, Hugging Face, LangGraph, or LangChain.
• Experience with model serving, inference optimization, or AI platform tools such as MLflow, Kubeflow, vLLM, or similar.
• Experience with simulations for scientific or engineering projects, particularly physical systems simulations.
• Experience with GPU-based systems or running AI models in HPC environments.
• Experience writing and deploying MCP Servers on Kubernetes
• DoD experience
• Secret Security Clearance – Active or Inactive

Education:

• Bachelor’s degree in CS, Software Engineering or other IT-related field or equivalent experience

REMOTE WORK NOTICE: This position may be performed fully remote, hybrid, or onsite at an ARA office. Preference will be given to candidates located onsite in the Albuquerque, NM and Raleigh, NC area.