1 day ago
Base Salary
$168k - $322k/yr
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 lifecycle management.
- Translate business and technical requirements into user stories, technical designs, integrations, testing, UAT support, issue remediation, development activities, and hypercare models.
- Build production-grade agentic AI solutions and integrate them with enterprise IT, operations data sources, and platforms such as ERP, CRM, and ITSM systems.
- Drive architectural decisions across on-premises, cloud, hybrid, and containerized deployment models.
- Define agent autonomy boundaries, human-in-the-loop workflows, escalation strategies, and multi-agent coordination patterns.
- Design observability and evaluation frameworks for latency, accuracy, cost, failure modes, and hallucination analysis.
- Lead technical design reviews, documentation, integration testing, and collaboration with enterprise IT, product, and research teams.
Requirements
- Bachelor’s degree in Computer Science or a related field, or equivalent experience.
- 8+ years of demonstrable experience in software architecture, systems design, or enterprise platform engineering.
- Strong hands-on Python expertise, with experience in SQL, JSON, and backend service development; Java is a plus.
- Proficiency in AI/ML systems, generative AI, or agentic AI frameworks, including LLMs, RAG architectures, and agent orchestration.
- Experience with vector databases, embeddings, retrieval optimization, indexing strategies, similarity search tuning, and production relevance evaluation.
- Experience designing multi-agent communication and coordination patterns such as planner/executor/critic and supervisor-agent architectures.
- Experience integrating enterprise platforms through APIs, data connectors, or custom services.
- Strong technical solution design, analytical, technical modeling, and business process modeling skills.
- Ability to collaborate with and influence cross-functional technical and business stakeholders and communicate complex architectural concepts clearly.
- Preferred experience with containerized deployments, Kubernetes, hybrid cloud/on-premises environments, NVIDIA NeMo, Guardrails, AIQ, GPU-optimized inference stacks, observability tools, telemetry dashboards, AI-agent evaluation frameworks, supply chain AI applications, and technical thought leadership.
Benefits
- Eligible for equity and benefits.
- Work location and arrangement are not specified.
- Applications will be accepted at least until November 6, 2026.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.