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…