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

General Dynamics Information Technology•Chantilly, VA

4d•$161,500 - $218,500•Onsite

About The Position

GDIT is 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. The role sits at the intersection of AI engineering, cloud and platform engineering, security, data, and Service Operations, ensuring advanced AI capabilities can be deployed and operated safely at scale in highly regulated environments. The engineer designs and manages a controlled model and software supply chain, implements local inference services, and orchestrates repeatable release trains for AI workloads. The position establishes standards for observability, capacity management, and safe rollback in environments where imports, deployments, and configuration changes are governed events. The role is onsite in Chantilly, Virginia, and requires an active TS/SCI clearance with Polygraph and U.S. citizenship.

Requirements

Bachelor’s degree in computer science, software engineering, machine learning, information technology, or a related field, or approved equivalent experience.
Active TS/SCI with Polygraph clearance.
U.S. citizenship required.
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.

Nice To Haves

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 or optimization.
Experience with Azure Government, AWS GovCloud, C2S-equivalent, or on-premises platform delivery.
Role-relevant CKA/CKS, current Red Hat platform engineering credentials, Microsoft Certified: DevOps Engineer Expert, or relevant Terraform, NVIDIA, or ML-platform credentials.
Experience integrating model-quality or evaluation signals with deployment gates and production monitoring.

Responsibilities

Design and operate AI/ML platform services for local model serving, orchestration, model and artifact registries, experiment and 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 and signing, SBOMs, malware and 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 models, tokenizers, prompts, retrieval indexes, embedding models, configurations, evaluation sets, applications, and infrastructure.
Develop GPU, CPU, storage, and 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 and recovery, continuity, patching, vulnerability treatment, certificate and 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.

Benefits

Comprehensive benefits and wellness packages
401(k) plan with company match
Competitive pay
Paid time off
Medical plan options, some with Health Savings Accounts
Dental plan options
Vision plan
Full flex work weeks where possible
Vacation, sick and personal time, holidays, paid parental, military, bereavement, and jury duty leave
Short- and long-term disability benefits
Life insurance
Accidental death and dismemberment insurance
Personal accident, critical illness, and business travel and accident insurance