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Tech Stack
Tools & technologiesCloudKubernetesPythonPyTorch
About the role
Key responsibilities & impact- Lead deployment, integration, and operational support of AI platforms, tools, and services
- Design, implement, monitor, and optimize AI infrastructure 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 technical support, troubleshooting, root cause analysis, and documentation for AI platforms and user-facing AI services
- Maintain observability through logging, metrics, performance monitoring, alerting, and incident response
- Ensure security, compliance, and governance requirements, including audits, vulnerability management, and secure architecture reviews
- Assess and implement enhancements for performance, scalability, reliability, and cost efficiency
- Collaborate across divisions to align technical implementations with mission and business objectives
- Evaluate emerging AI tools, frameworks, and infrastructure approaches
- Develop and maintain technical documentation, runbooks, architecture diagrams, and operational procedures
Requirements
What you’ll need- Bachelor’s degree in computer science, Engineering, Information Technology, or a related STEM field, or equivalent experience
- 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 ability to collaborate effectively across technical and non-technical teams
- Secret Security Clearance – Active or Inactive
- Preferred: advanced degree or certifications related to AI or machine learning
- Preferred: experience integrating AI models into scientific workflows
- Preferred: familiarity with large language model APIs and orchestration frameworks such as OpenAI, Hugging Face, LangGraph, or LangChain
- Preferred: experience with model serving, inference optimization, or AI platform tools such as MLflow, Kubeflow, vLLM, or similar
- Preferred: experience with simulations for scientific or engineering projects, particularly physical systems simulations
- Preferred: experience with GPU-based systems or running AI models in HPC environments
- Preferred: experience writing and deploying MCP Servers on Kubernetes
- Preferred: DoD experience
Benefits
Comp & perks- Fully remote, hybrid, or onsite work options
- Opportunity to work on diverse AI initiatives
- Professional collaboration across technical and non-technical teams
- Technical development involving emerging AI tools, frameworks, and infrastructure approaches