Distinguished Engineer AI Infrastructure Architecture

About Splunk, a Cisco Company

Splunk, a Cisco company, is dedicated to building a safer and more resilient digital world. We offer an end-to-end full-stack platform designed for hybrid, multi-cloud environments. Leading enterprises leverage our unified security and observability platform to maintain the security and reliability of their digital systems. Join us in empowering organizations to excel, while you achieve new career heights with a supportive team.

Meet the Team

Our Distinguished Engineer team is instrumental in driving the architecture and technical direction for Splunk's $3.5B platform, which processes petabytes of data for over 95% of the Fortune 100. As a cohesive group reporting to the VP of Architecture, we operate with autonomy while closely coordinating to deliver practical, durable solutions that evolve the platform for future demands. Each Distinguished Engineer owns cross-organizational domains, which adapt fluidly to emerging needs. You will collaborate with exceptionally smart engineers who prioritize rigorous thinking and friendly teamwork, shaping the technical future of our platform!

Impact of a Distinguished Engineer AI Infrastructure Architecture

In this role, you will:

• Architect and operationalize AI infrastructure as a foundational component of Cisco Data Fabric, enabling engineering teams to seamlessly integrate AI capabilities across the world's largest and most diverse data sets.
• Design MLOps platforms that adeptly tackle complex lifecycle challenges, including managing embedding model migrations, ensuring version compatibility, and facilitating continuous model updates.
• Ensure operational excellence through comprehensive monitoring, robust governance, and high-scale serving at petabyte scale.
• Identify and drive new opportunities for leveraging AI across the entire stack, establishing consistent architectural patterns for agent prompts, tool integration, and model orchestration.
• Build scalable data pipelines and operational frameworks, empowering hundreds of engineers to confidently deploy both Large Language Models (LLMs) and traditional Machine Learning models into production.

Success will be measured by customer adoption and usage of AI-powered features, along with critical operational metrics such as model performance, inference latency, and deployment velocity, all directly contributing to customer value.

Minimum Qualifications

• Bachelor's degree in Computer Science (or equivalent) with 15+ years of related experience; or Master's with 12+ years; or PhD with 8+ years or equivalent practical experience.
• Demonstrated experience designing and deploying AI/ML infrastructure and features successfully in production cloud environments.
• Hands-on experience with major AI services including OpenAI, Anthropic, HuggingFace, AWS Bedrock, Azure OpenAI Service, or similar platforms.
• Production-level experience with AWS, Azure, or GCP cloud platforms.
• Proven track record of leading technical decisions and architectural direction across engineering organizations comprising 50+ engineers.

Preferred Qualifications

• Experience with LLM-specific infrastructure, including agent frameworks, prompt management, and tool integration.
• Expertise in model serving at scale, with significant experience in inference optimization and performance monitoring.
• Designed and deployed AI/ML infrastructure for on-premises environments.
• Proficiency with major ML frameworks such as TensorFlow, PyTorch, etc., and various model formats.
• Proven track record of mentoring and fostering the growth of engineers, coupled with strong collaboration skills.

Key skills/competency

• AI Infrastructure
• MLOps
• Large Language Models (LLMs)
• Cloud Architecture
• Data Pipelines
• Scalability
• System Design
• OpenAI/Anthropic/HuggingFace
• AWS/Azure/GCP
• Technical Leadership