Senior AI Engineer

NVIDIA•Santa Clara, CA

3d•$168,000 - $322,000

About The Position

The Senior AI Engineer will join NVIDIA Operations’ AI & Automation Supply Chain Business Transformations team to develop agentic AI for supply chain operations. The role implements decision logic and decision intelligence to deliver trusted, scalable, and innovative AI agents. The engineer will develop enterprise-grade agentic AI systems, including multi-agent architectures, orchestration frameworks, RAG pipelines, deployment, monitoring, and lifecycle management. The role is hands-on and includes requirements capture, technical design, integration testing, UAT support, development team coordination, and creation of a Hypercare support model. The engineer will partner with Enterprise IT engineering, product, and research teams, use LLMs and agentic frameworks including NVIDIA NeMo and AIQ, and ensure secure integration with enterprise IT and Operations data sources. The role also drives architectural decisions for on-premises, cloud, hybrid, and containerized deployments; leads technical design reviews and documentation; defines agent autonomy boundaries and human-in-the-loop workflows; and designs observability and evaluation frameworks for agent performance.

Requirements

Bachelor’s degree in Computer Science or a related field, or equivalent experience, and 8 or more years of demonstrable experience in software architecture, systems design, or enterprise platform engineering.
Strong hands-on expertise in Python for production-grade AI systems, with experience in SQL, JSON, and backend service development; Java is a plus.
Proficiency in AI/ML systems, generative AI, or agentic AI frameworks.
Experience 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, such as planner/executor/critic and supervisor agents.
Experience integrating enterprise platforms such as ERP, CRM, and ITSM with APIs, data connectors, or custom services.
Strong technical solution design and analytical skills, and ability to perform technical and business process modeling.
Excellent collaboration skills, including the ability to influence cross-functional stakeholders and build trusted partnerships.
Ability to communicate complex architectural concepts clearly to technical and business audiences.

Nice To Haves

Hands-on experience with containerized deployments, Kubernetes, and hybrid cloud/on-premises environments.
Python prototyping skills and 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.
Experience solving real-world problems with AI in the supply chain domain.

Responsibilities

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 them into user stories, perform technical design, drive end-to-end integration testing, support data setup and issue remediation during UAT, manage development team activities, and develop a Hypercare support model.
Implement enterprise-grade agentic AI supply chain solutions hands-on.
Partner with Enterprise IT engineering, product, and research teams while using LLMs, agentic frameworks, and NVIDIA NeMo and AIQ technologies.
Integrate solutions with enterprise IT and Operations data sources and agentic platforms, with a strong focus on content security.
Drive architectural decisions across on-premises, cloud, hybrid, and containerized deployment models 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 covering latency, accuracy, cost, failure modes, and hallucination analysis.