Senior Staff AI Platform Engineer
Required skills
10+ years in cloud, platform, or SRE roles with relevant education or equivalent experience, Bachelors degree or equivalent experience, Strong Python and at least one systems language (C++, Go, or Rust), with proven distributed systems debugging expertise, Deep experience building and scaling distributed systems, including Kubernetes and bare-metal infrastructure, Strong observability design across infrastructure and AI workloads (metrics, logging, tracing, AI quality signals), Hands-on experience operating AI/ML platforms, including MLOps, model serving, and GPU-accelerated environments, Experience with infrastructure and application security practices, such as identity/auth, network segmentation, supply chain security, and vulnerability management in cloud-native environments, Practical use of AI-assisted development tools and coding agents in daily workflows, Solid foundation in data structures, algorithms, and complexity analysis, Excellent problem-solving, communication, and collaboration across multiple functions
Preferred skills
Deep experience with AI/ML platforms (e.g., Hugging Face, Weights & Biases, NVIDIA NIM), Proven use of AI agents and LLM tooling to enhance observability, incident response, or developer productivity, Experience with artifact management, AI supply chain security, or trusted model distribution systems, Experience with AI-specific threat models (OWASP Top 10 for LLMs, model poisoning, adversarial inputs), experience with FedRAMP, SOC 2, or other compliance frameworks relevant to your environment, and red-teaming or security evaluation of LLM systems, Strong ownership demeanor with a structured, automation-first approach, Demonstrated impact driving AI-first engineering practices across teams
Technologies
Python, C++, Go, Rust, Kubernetes, bare-metal infrastructure, MLOps, model serving, GPU-accelerated environments, AI/ML platforms, observability tools, AI-assisted development tools, coding agents, artifact management, AI supply chain security, trusted model distribution systems, AI-specific threat models, FedRAMP, SOC 2, compliance frameworks, red-teaming, security evaluation of LLM systems
Responsibilities
Define and lead AI-native infrastructure roadmaps and cross-organizational initiatives, Architect and scale LLM/ML infrastructure across cloud-native clusters and on-premises hardware, Design and implement observability for infrastructure health and AI model performance, Build LLM-aware monitoring and leverage AI to improve incident response and reduce toil, Develop automation and tooling to ensure reliability, scalability, and developer self-services, Troubleshoot complex distributed systems, including deep Kubernetes and AI/ML scaling challenges, Drive AI-assisted engineering practices and mentor engineers to foster an AI-first culture, Partner with product engineering and internal business units to translate AI platform capabilities into reliable, scalable solutions that accelerate product development
Seniority
Senior
Domain
AI/ML, Cloud, Infrastructure, Security, SRE
