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.
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
- Bachelor’s degree in computer science, Software Engineering, Machine Learning, Information Technology, or related field, or approved equivalent experience.
- 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.
Benefits
- Comprehensive benefits and wellness packages
- 401K with company match
- Competitive pay and paid time off
- Award-winning culture of innovation and a military-friendly workplace