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

Clearance Level Must Currently Possess:

Clearance Level Must Be Able to Obtain:

Job Family:

• 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.

• 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 Hat platform engineering credentials, or Microsoft Certified: DevOps Engineer Expert. Relevant Terraform, NVIDIA or ML-platform credentials
• Experience integrating model-quality/evaluation signals with deployment gates and production monitoring.

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.

• →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.

Work Location:

Additional Work Locations:

gdit.com/tc.