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
• Collaborate with SW leads and Technical Program Manager to qualify releases
• Attract and help build downstream production engineering talent
• Role‑model and foster a culture of humility and innovation for product delivery

• 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

• Master's or similar qualification in a relevant field
• Experience with scalable test and automation infrastructure to product ionize 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

Mid‑Senior level

Full‑time

Information Technology and Engineering

Technology, Information and Media, Software Development, and IT Services and IT Consulting