Senior Software Engineer, AI Platform

About Splunk

Join us as we pursue our disruptive new vision to make machine data accessible, usable and valuable to everyone. We are a company filled with people who are passionate about our product and seek to deliver the best experience for our customers. At Splunk, we’re committed to our work, customers, having fun and most importantly to each other’s success. Learn more about Splunk careers and how you can become a part of our journey!

Role

As the Senior Software Engineer, AI Platform in the Splunk Artificial Intelligence group, you will be responsible for leading the development of the AI platform that powers the core AI/ML capabilities in the Splunk product portfolio. The Splunk AI/ML capabilities drive productivity and automation in our customers’ journey to digital resiliency. You will collaborate with cross-functional teams, mentor junior team members, and help drive the core AI/ML tooling roadmap for the Splunk AI group.

Requirements

Knowledge, Skills, and Abilities

• 7+ years of practical experience in Software Architecture, design, implementation and production deployment of distributed systems using microservices architecture and APIs.
• Expert in at least one high level language such as Java/C++ (Java preferred)
• Proficient in at least one scripting language, such as Python or Go.
• In-depth knowledge and proven track record in AI/ML infrastructure and tooling, including GPU infrastructure for gen-AI, frameworks for inference and training/fine-tuning, frameworks for agentic orchestration.
• In-depth knowledge and proven track record in distributed systems and data platforms, including Ray & vector databases.
• Familiarity with architectural patterns of large, high-scale applications.
  
  

Responsibilities

As a senior member of the AI Platform, you will be involved in all aspects of the development and operations of Splunk's Generative AI Service.

• Create high-quality Generative AI services to deliver exceptional value to customers.
• Build scalable infrastructure, including microservices and backend systems for UI, dashboards, and other interactive applications.
• Deploy and scale Generative AI models on Ray clusters.
• Collaborate with applied scientists and fellow software engineers to enhance other parts of the infrastructure.
• Experience with cloud-native technologies, such as Kubernetes, Docker, and server-less computing.
  
  

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