6 days ago
Responsibilities
- Design, develop, and maintain scalable full-stack solutions for enterprise sales workflows.
- Guide technical solutions across front-end, back-end, APIs, data services, integrations, and cloud infrastructure.
- Translate product requirements and business needs into secure, maintainable solutions and intuitive user experiences.
- Integrate generative AI models, AI services, APIs, retrieval systems, and agentic workflows into production applications.
- Design architectures supporting performance, availability, observability, security, scalability, and maintainability.
- Lead technical design discussions, evaluate implementation approaches and emerging technologies, and make architecture decisions.
- Improve testing, code quality, continuous integration and delivery, monitoring, documentation, and production readiness.
- Investigate complex issues, improve reliability and user experience, mentor engineers, and contribute to engineering standards.
Requirements
- Bachelor’s degree or equivalent experience in Computer Science, Engineering, or a related technical field is encouraged.
- 10+ years of professional software engineering experience building and operating production applications.
- Experience developing full-stack applications with modern front-end, back-end, and web application technologies.
- Proficiency in one or more of Python, Java, JavaScript, React, Node.js, or similar technologies.
- Experience designing APIs, distributed applications, data services, enterprise integrations, scalable cloud applications, databases, and messaging systems.
- Experience integrating AI or machine learning capabilities through APIs, models, retrieval systems, or AI services.
- Knowledge of software architecture, system design, security, testing, observability, and production operations.
- Ability to lead technical initiatives, make engineering decisions, communicate with technical and non-technical teams, and mentor engineers.
- Preferred experience with generative AI applications, AI assistants, retrieval-augmented generation, agentic workflows, AI productivity tools, Python-based AI services, large language model APIs, vector databases, prompt engineering, or AI evaluation.
- Preferred familiarity with responsible AI, data privacy, access controls, enterprise AI governance, Kubernetes, containers, microservices, infrastructure as code, DevOps practices, reliability, scalability, observability, and cloud cost efficiency.
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About Nvidia
Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.