AI Embedded Engineer

Responsibilities

• Design and develop high-performance AI frameworks for large-scale distributed computation
• Optimize scalability and efficiency using Nvidia Dynamo Framework
• Work with distributed dataflow programming to orchestrate GPU workloads using Python and Kubernetes
• Integrate advanced LLMs into real-world applications, shaping the future of AI-driven software
• Contribute to building test-automation infrastructure for Kubernetes on large-scale GPU clusters.
• Help develop detailed test plans for different milestones and operationalize them in test-automation infrastructure.
• Own and conduct end-end system, scale and stress testing.
• Working together with SW leads and Technical Program Manager, qualify the releases.
• Attract and help build downstream production engineering talent.
• Role model and foster a culture of humility and innovation for product delivery.

Experience:

• 3–8 years of experience in software engineering, ideally at a staff level
• Strong expertise in distributed dataflow programming and distributed systems
• Hands-on experience with LLMs and AI frameworks
• Proficiency in Python, with experience orchestrating GPU workloads
• Experience with Kubernetes for containerized application deployment and orchestration
• Experience working in systems & systems SW, Cloud and Kubernetes.
• Experience with production-testing and automation of Kubernetes deployments.

Preferred Qualifications:

• Master's or similar qualification in a relevant field.
• Experience with scalable test and automation infrastructure to productionize workloads.
• Experience with GPU platforms (e.g., Nvidia DGX, H100) and high-performance computing environments.
• Experience triaging customer bugs, prioritizing, and resolving issues in production.
• Familiarity with AI developer frameworks, tools, and automation systems