Senior AI Systems Engineer

Description

Essential Functions:

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

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

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

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

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

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

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

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

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

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

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

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

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Experience and Skills Required:

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

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

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

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

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

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

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

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  Familiarity with AI security functionality in enterprise environments including OAuth

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  Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.

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Preferred:

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  Advanced degree or certifications related to AI or machine learning.

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  Experience integrating AI models into scientific workflows.

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  Familiarity with large language model (LLM) APIs and orchestration frameworks such as OpenAI, Hugging Face, LangGraph, or LangChain.

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  Experience with model serving, inference optimization, or AI platform tools such as MLflow, Kubeflow, vLLM, or similar.

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  Experience with simulations for scientific or engineering projects, particularly physical systems simulations.

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  Experience with GPU-based systems or running AI models in HPC environments.

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  Experience writing and deploying MCP Servers on Kubernetes

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  DoD experience

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  Secret Security Clearance – Active or Inactive

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Education:

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

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