Principal Software Engineer, Retail and CPG Platform Architecture
$240k - $380k • Remote • US, CA, Santa Clara
Posted 1mo ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Agent Engineer
About the job
We are seeking a technical architecture expert with hands-on development experience to define and build the Retail and Consumer Packaged Goods Platform. This role involves leveraging NVIDIA's existing retail and supply chain blueprints, along with proven workflows and best practices, to architect a platform that powers digital commerce, supply chain, and intelligent stores. The platform will be built on NVIDIA's full stack, including Agentic AI and accelerated computing, and will utilize NVIDIA Nemo microservices and Nemotron open-source models, with an initial focus on Agentic AI for supply chain, digital commerce, and employee productivity.
Responsibilities
- Define the product and technical vision and architecture for the Retail & CPG platform.
- Develop platform strategy across supply chain, stores, and e-commerce.
- Architect and build systems hands-on, utilizing existing Retail and Agentic Commerce, and supply chain blueprints.
- Build reusable reference architectures for industry adoption.
- Use NVIDIA technologies like NeMo, NIM, RAPIDS, and cuOpt to write code and create the platform.
- Deliver a production-ready platform focusing on latency, throughput, and cost efficiency.
- Ensure system reliability through monitoring, evaluation, and fallback strategies.
- Present the platform to ISVs, GSIs, and customers.
- Contribute to technical thought leadership and engage with the industry.
Requirements
- Bachelor of Science or equivalent experience in computer science or electrical engineering.
- 15+ years of dynamic work experience within high growth tech companies or building AI and agentic solutions for a major Retailer.
- Proven track record of writing code and shipping systems, with experience as a developer of AI agents.
- Developer expertise in AI systems, including LLMs, RAG, and agent-based systems.
- Strong understanding of data pipelines, embeddings, and systems architecture.
- Experience with distributed systems, APIs, and microservices.
- Demonstrated success in lean, high-velocity environments, collaborating with engineering.
Benefits
- Equity
- Benefits