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.


• 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.