What are the responsibilities and job description for the AI Embedded Engineer position at VeeAR Projects Inc.?
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