Join the AI & Automation Supply Chain Business Transformations team in NVIDIA Operations to develop the future of agentic AI at scale. As the Senior AI Engineer in Operations, you implement our vision for codifying our decision logics and implement decision intelligence ensuring the AI Agents delivers value that are trusted, scalable, and innovative. This is a rare opportunity to define the AI agents for Supply Chain Operations inside one of the world’s most advanced AI companies. If you thrive at the intersection of AI innovation, and Supply Chain transformation, join us and make your mark on the future of agentic AI.
What you will be doing:
- Develop and own enterprise-grade Agentic AI systems for Supply Chain use cases, including multi-agent architectures, orchestration frameworks, RAG pipelines, deployment, monitoring, and full lifecycle management.
- Capture design requirements, translate requirements into user stories, perform technical design, drive end to end integration testing, support data set up and issue remediation during UAT, manage development team activities, develop Hypercare support model
- Be hands on in development and implement enterprise grade Agentic AI Supply Chain solutions
- Partner with Enterprise IT engineering, product, and research teams while using LLMs, agentic frameworks, and NVIDIA’s own NeMo and AIQ technologies.
- Ensure integration with enterprise IT and Operations data sources and Industry’s best Agentic platforms with strong content security focus.
- Drive architectural decisions across deployment models (on-prem, cloud, hybrid, containerized) to deliver scalable, reliable, and efficient solutions.
- Lead technical design reviews, develop technical design documentation, and follow principles of code development.
- Define agent autonomy boundaries, human-in-the-loop workflows, and escalation strategies for enterprise supply chain use cases.
- Design and implement agent observability and evaluation frameworks (latency, accuracy, cost, failure modes, and hallucination analysis).
What we need to see:
- Bachelor’s degree in Computer Science or related field (or equivalent experience) and 8+ years of demonstrable experience in software architecture, systems design, or enterprise platform engineering.
- Strong hands-on expertise in Python for building production-grade AI systems, with experience in SQL, JSON, and backend service development (Java a plus)
- Demonstrate proficiency in AI/ML systems, generative AI, or agentic AI frameworks.
- Experience working with LLMs, RAG architectures, and agent orchestration frameworks such as ReAct patterns, LangChain-based systems, and AutoGPT-style workflows.
- Hands-on experience with vector databases, embeddings, and retrieval optimization, including indexing strategies, similarity search tuning, and relevance evaluation for production systems.
- Experience designing multi-agent communication and coordination patterns (planner/executor/critic, supervisor agents, etc.).
- Experience integrating enterprise platforms (e.g., ERP, CRM, ITSM) with APIs, data connectors, or custom services.
- Strong technical solution design, analytical skills, and the ability to perform technical and business process modeling
- Excellent collaboration skills with the ability to influence cross-functional stakeholders and build trusted partnerships.
- Ability to communicate complex architectural concepts clearly and inspire confidence across technical and business audiences.
Ways to stand out from the crowd:
- Hands-on experience with containerized deployments, Kubernetes, and hybrid cloud/on-prem environments.
- Prototyping skills in Python with ability to build proof-of-concept agent workflows.
- Experience with NVIDIA AI technologies such as NeMo, Guardrails, AIQ, or GPU-optimized inference stacks.
- Track record of publishing technical papers, architecture patterns, or thought leadership in AI systems.
- Knowledge of observability tools, telemetry dashboards, and evaluation frameworks for AI agent performance as well as experience solving real-world problems with AI in supply chain domain.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and creative people in the world working here! If you are driven by impact, passionate about enterprise AI, and inspired to shape the agentic AI at scale for NVIDIA’s Supply Chain Operations, we want to hear from you. Apply today!
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:168,000 USD – 270,250 USD for Level 4; and 200,000 USD – 322,000 USD for Level 5.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- Bachelor’s degree in Computer Science or a related field, or equivalent experience
- 8+ years of experience in software architecture, systems design, or enterprise platform engineering
- Strong hands-on Python experience building production-grade AI systems
- Experience with SQL, JSON, and backend service development
- Proficiency in AI/ML systems, generative AI, or agentic AI frameworks
- Experience with LLMs, RAG architectures, and agent orchestration frameworks
- Experience with vector databases, embeddings, retrieval optimization, indexing strategies, similarity search, and relevance evaluation
- Experience designing multi-agent communication and coordination patterns
- Experience integrating enterprise platforms such as ERP, CRM, or ITSM through APIs, data connectors, or custom services
- Strong technical solution design, analytical, and technical and business process modeling skills
- Excellent collaboration and cross-functional stakeholder influence skills
- Ability to communicate complex architectural concepts clearly to technical and business audiences
- Experience with containerized deployments, Kubernetes, and hybrid cloud or on-premises environments
- Python prototyping and proof-of-concept agent workflow development experience
- Experience with NVIDIA AI technologies such as NeMo, Guardrails, AIQ, or GPU-optimized inference stacks
- Published technical papers, architecture patterns, or AI systems thought leadership
- Knowledge of observability tools, telemetry dashboards, and AI agent evaluation frameworks
- Experience solving real-world supply chain problems with AI
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
