Senior Principal Platform Engineer - AI Automation

As a Platform Engineering Architect you will focus on operationalizing, securing, and maturing artificial intelligence capabilities by building and maintaining the AI "path-to-prod" and a scalable AI run stack—the AI “plumbing.” Your work centers on providing a unified interface for AI model access, integrating foundational and reasoning models, AI agents, and generative AI. Key responsibilities include contributing to the K8s-based AI access platform, managing deployment of core AI services, integrating frontier models (e.g., Claude, GPT) and local inference engines (e.g., vLLM) along with designing MLOps pipelines. The role also requires providing expert recommendations for enterprise AI adoption, specifically in agentic orchestration and spec-driven development.

Core Requirements
·      Professional Experience: 7+ years of combined experience in DevSecOps, Platform Engineering, or SRE.
·      Kubernetes Mastery: Deep expertise in Administration and Development of Kubernetes clusters.
·      Containerization: Advanced knowledge of Docker or equivalent container build tools.
·      Cloud & IaC: Experience with Azure or AWS architecture and Infrastructure as Code (Terraform/Crossplane).
·      Software Development: Proficiency in at least one backend or scripting language—ideally Python, Go, or Bash—to drive systems automation.
·      Core Networking: Solid understanding of OSI Layer 4–7, including VPC/VNET configuration, DNS, Load Balancing, and SSL/TLS management.
·      AI Ecosystem: Practical experience with modern AI/ML frameworks and tooling such as PyTorch, Hugging Face, LangChain, vLLM, Ray, MLflow, or equivalent open-source ecosystems.
·      LLM & AI Infrastructure: Hands-on experience deploying, scaling, and securing AI/ML workloads on Kubernetes, including GPU-enabled clusters, model-serving platforms, and distributed inference/training systems.
·      AI Platform Operations: Experience building internal AI platforms or developer enablement tooling that supports model lifecycle management, experimentation, inference endpoints, and reproducible AI workflows.
·      MLOps & AI Delivery Pipelines: Familiarity with MLOps concepts and tooling, including automated model deployment, versioning, evaluation, observability, rollback strategies, and CI/CD integration for AI systems.Vector & Retrieval Systems: Working knowledge of vector databases, embedding pipelines, retrieval-augmented generation (RAG), and semantic search architectures.
·      AI Security & Governance: Understanding of AI security concerns including model isolation, data handling controls, prompt injection risks, supply-chain security, and governance requirements for sensitive or regulated environments.

Primary Technical Focus
Heavy emphasis is placed on the GitLab ecosystem and GitOps workflows:
·      GitLab Expert: Mastery of GitLab CI/CD (including Runners, Templates, and Security Scanners).
·      GitOps & Continuous Delivery: Hands-on experience with ArgoCD or equivalent declarative CD tools.
·      Package Management: Proficiency with Helm for templating and deploying Kubernetes applications.

Preferred Skills & Certifications
·      Security Compliance: Possession of DoD 8570 certifications (e.g., Security+ or CASP+/SecurityX).
·      Advanced Networking: Experience with Service Mesh technologies (e.g., Istio, Cilium) and CNI plugins.
·      Identity & Access: Configuration and management of Keycloak or OIDC/SAML providers.
·      Observability: Experience with the Grafana / Prometheus stack or similar.
·      Database Administration: Functional knowledge of PostgreSQL and MySQL.

Bonus Qualifications
·      Data Engineering: Experience maintaining data pipelines or high-throughput data infrastructure.
·      Cybersecurity: Background in threat modeling, vulnerability management, or SOC operations.
·      API Design: Experience designing or maintaining RESTful or RPC APIs.