AI Senior Staff Systems Engineer

We are seeking a highly skilled and experienced AI Systems Engineer to join our team. This is a hands-on, senior individual contributor role that will be pivotal in leading the development, operations, and support of our entire AI infrastructure. You will be responsible for the entire lifecycle of our AI systems, from architecting and building high-performance GPU clusters to deploying and optimizing our most advanced AI models and agentic services.

Responsibilities

• AI Infrastructure Architecture & Strategy: Lead the design and implementation of our next-generation AI infrastructure to support our Agentic AI initiatives. You will define the technical strategy for our on-premise GPU clusters, storage solutions, and networking to ensure optimal performance, scalability, and reliability for all our AI workloads.
• Cloud AI Service Integration: Support and secure the use of public cloud AI services, including Azure OpenAI services and Google Cloud Platform (GCP) services like Gemini. This includes managing secure access, monitoring usage, and tracking billing to ensure cost-effectiveness. You will also have hands-on experience supporting compute, GPUs, and AI services on both GCP and Azure.
• Hands-on GPU Cluster Management: Take a leadership role in the configuration, installation, and optimization of GPU server clusters. This includes advanced troubleshooting of hardware and software, performance tuning, and implementing best practices for cluster utilization and resource management. You will be an expert in administering job schedulers like LSF in a production environment, including integration with Docker for containerized job submission.
• Full-Stack AI Tech Stack Development & Operations: Architect and deploy a robust and scalable AI tech stack. You will be responsible for the end-to-end operational lifecycle, including setting up and managing deep learning frameworks (PyTorch, TensorFlow), containerization with Docker and Kubernetes, and implementing CI/CD pipelines for AI model development.
• Advanced LLM Deployment & Optimization: Lead the deployment, serving, and optimization of Large Language Models (LLMs). You will be an expert in techniques such as model quantization, distillation, and using high-performance serving frameworks (e.g., vLLM, TGI, TensorRT-LLM) to maximize inference throughput and minimize latency.
• Agentic AI Workflow & Service Engineering: Architect and build production-grade Agentic AI workflows and services. You will be responsible for the technical design and implementation of systems that integrate LLMs with external tools, APIs, and databases, and will mentor other engineers on building robust and scalable AI agent applications.
• Automation & Monitoring: Develop and maintain automation scripts using languages like Python, Bash, or Perl to streamline system maintenance, deployment, and reporting. Implement and manage monitoring solutions for system health, job statuses, GPU utilization, and container performance to proactively identify and resolve issues.
• AI Systems Support & Mentorship: Act as the final escalation point for the most complex technical issues related to our AI infrastructure. You will also serve as a technical leader and mentor to other engineers, providing guidance on best practices in AI systems engineering, performance tuning, and operational excellence.
• Security and Compliance: Develop and implement security best practices for our AI systems and data, ensuring compliance with relevant regulations and protecting our intellectual property.

Required Skills and Qualifications

• 10+ years of experience in a senior technical role, with at least 5 years focused on building and operating high-performance computing or AI infrastructure. Proven track record as a Principal or Senior Staff Engineer.
• Expert-level knowledge of NVIDIA GPU architecture and technologies like CUDA and cuDNN. Extensive experience with multi-GPU and multi-node training and inference.
• Proven experience with public cloud AI services, specifically managing access, usage, and billing for Azure OpenAI and Google Cloud Platform (GCP) services.
• Extensive hands-on experience with Docker: image management, container orchestration, and troubleshooting.
• Proficiency in scripting languages such as Python, Bash, or Perl.
• Deep expertise in Linux system administration (RHEL preferred), including networking, storage, and performance tuning.
• Familiarity with user authentication and integration using systems like LDAP or Active Directory.
• Strong problem-solving and communication skills with the ability to work in a multi-platform, cross-functional, and geographically distributed team.

Preferred/Bonus Skills

• Understanding of AI job profiling and tuning (memory, GPU, I/O).
• Experience administering LSF clusters in a production or research environment. Familiarity with other job schedulers like Slurm is a plus.
• Experience with LSF Docker integration and job submission using container images.
• Experience with macOS/AppleSilicon system admin tasks and troubleshooting.

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