Principal AI Platform and Model Operations Engineer - TS/SCI with Polygraph

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

Type of Requisition: Regular

Clearance Level Must Currently Possess: Top Secret SCI + Polygraph

Clearance Level Must Be Able to Obtain: Top Secret SCI + Polygraph

Public Trust/Other Required: None

Job Family: Data Science and Data Engineering

Job Qualifications:

Skills: Artificial Intelligence (AI), DevSecOps, Machine Learning (ML)

Certifications: None

Experience: 8 + years of related experience

US Citizenship Required: Yes

Job Description

We are seeking a hands-on AI Platform Operations Engineer to own and mature the end-to-end platform and lifecycle mechanisms that move AI models, prompts, retrieval assets, dependencies, and services from approved development into reliable, classified production environments. This role sits at the intersection of AI engineering, cloud/platform engineering, security, data, and Service Operations, ensuring that advanced AI capabilities can be deployed and operated safely at-scale in highly regulated environments.

You will be responsible for designing and managing a controlled model and software supply chain, implementing local inference services, and orchestrating repeatable release trains for AI workloads. You will establish and enforce standards for observability, capacity management, and safe rollback, operating in environments where every import, deployment, and configuration change is a governed event. This position is ideal for someone who thrives on building robust, secure, and compliant AI platforms that turn experimental solutions into mission-ready, production-grade services.

Key Responsibilities

• Design and operate AI/ML platform services for local model serving, orchestration, model and artifact registries, experiment/evaluation tracking, feature or retrieval assets, and deployment automation.
• Build controlled import and release workflows for model weights, containers, Python packages, libraries, prompts, datasets, and vulnerability updates in disconnected environments.
• Implement provenance, checksums/signing, SBOMs, malware/vulnerability scans, license review, allowlists, approvals, staged validation, and rollback for AI artifacts.
• Deploy and tune inference services using approved serving stacks; manage batching, concurrency, memory, quantization, autoscaling where available, queueing, resilience, and graceful degradation.
• Establish versioning and release relationships among model, tokenizer, prompt, retrieval index, embedding model, configuration, evaluation set, application, and infrastructure.
• Develop GPU/CPU/storage/network capacity models and advise on hardware options, consolidation, scheduling, and cost/performance trade-offs.
• Implement health, latency, throughput, saturation, failure, quality, drift, and usage telemetry integrated with enterprise observability and ServiceNow.
• Define backup/recovery, continuity, patching, vulnerability treatment, certificate/secret rotation, and incident procedures for AI platform services.
• Partner with AI Assurance and Security to enforce use-case gates, evaluation thresholds, audit, data restrictions, and release evidence.
• Lead technical reviews, troubleshoot production AI platform incidents, mentor engineers, and document reusable platform patterns.

Required Qualifications

Education: Bachelor's degree in computer science, Software Engineering, Machine Learning, Information Technology, or related field, or approved equivalent experience.

Location: Onsite

Clearance: Active TS/SCI with Polygraph

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.

Strong Linux, container, Kubernetes/OpenShift, networking, storage, GPU, identity, secrets, and infrastructure automation skills.

Experience with model/artifact registries, experiment tracking, release management, CI/CD, internal package/container repositories, and disconnected dependency management.

Ability to size inference capacity using model precision, VRAM, context length, concurrency, throughput, latency, and availability requirements.

Strong Python and automation proficiency plus experience with Terraform, Ansible, Helm, GitLab CI/Jenkins, or comparable tooling.

Working knowledge of RMF/STIG, software supply-chain security, SBOMs, vulnerability management, logging/audit, and classified data handling.

Demonstrated technical leadership, incident judgment, cross-team coordination, mentoring, and clear documentation.

Preferred Qualifications

Experience with PyTorch, Hugging Face, MLflow, Kubeflow, Ray, NVIDIA GPU tooling, CUDA, or enterprise AI platform products.

Experience operating A100, H100, L40S, or comparable accelerators and performing quantization/optimization.

Experience with Azure Government, AWS GovCloud, C2S-equivalent, or on-premises platform delivery.

Role-relevant CKA/CKS, current Red H