Senior AI Platform & Model Operations Engineer

General Dynamics IT; Chantilly (VA); On-site; USD 140,000 - 190,000; Full time

Job summary

General Dynamics IT is seeking an experienced AI Platform Operations Engineer in Chantilly, VA, to own end-to-end platform lifecycle for deployed AI models in highly regulated environments. The role requires hands-on leadership in AI engineering, cloud/platform operations, security, data, and service operations.

You will design and manage controlled model and software supply chains, implement local inference services, and orchestrate repeatable release trains for AI workloads while enforcing

Qualifications

• Bachelor's degree in computer science, software engineering, ML, information tech, or related field.
• 8+ years of platform, DevSecOps, SRE, ML engineering, or cloud engineering experience, including 3+ years supporting production AI/ML platforms.
• Hands-on experience deploying and operating local model-serving platforms such as vLLM, TGI, Triton, TensorRT-LLM, llama.cpp, Ollama, ONNX Runtime, or comparable technologies.

Responsibilities

• Design and operate AI/ML platform services for local model serving, orchestration, and artifact registries.
• Build controlled import and release workflows for model weights, containers, Python packages, and datasets in disconnected environments.
• Implement provenance, SBOMs, vulnerability scans, license review, and rollback for AI artifacts.

Skills

Artificial Intelligence

DevSecOps

Machine Learning

Linux

Kubernetes/OpenShift

CI/CD

Education

Bachelor's degree in Computer Science or related field

Tools

vLLM

TGI

Triton

TensorRT-LLM

llama.cpp

ONNX Runtime

Job description

General Dynamics IT is seeking an experienced AI Platform Operations Engineer in Chantilly, VA, to own end-to-end platform lifecycle for deployed AI models in highly regulated environments. The role requires hands-on leadership in AI engineering, cloud/platform operations, security, data, and service operations.

You will design and manage controlled model and software supply chains, implement local inference services, and orchestrate repeatable release trains for AI workloads while enforcing