Senior AI Engineer, Enterprise GenAI & MLOps

Stanford University; Redwood City (CA); Sur place; USD 170 000 - 195 000; Plein temps

Résumé du poste

Stanford University recruiting an experienced AI/GenAI engineer to design, implement, and support AI solutions across university use cases. The role blends hands-on engineering with mentorship, prototyping and productionizing proofs of concept, and partnering with project managers, security, and infrastructure teams.

You will lead AI tracks, develop LLM-based agents, and improve workflows with new capabilities (LLMs, RAG, MLOps) while ensuring governance and security.

Qualifications

• Bachelor's degree and eight years relevant experience.
• Experience building and shipping production AI/ML solutions.
• Strong knowledge of LLMs, transformers, and ML concepts.
• Proficient in Python; familiarity with Node.js/Next.js/React/TypeScript and Java.
• Experience with cloud AI stacks (Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search tech.
• Excellent communication and ability to work with cross‑functional teams.

Responsabilités

• Translate requirements into well-engineered AI components and pipelines.
• Build and maintain LLM-based agents/services calling enterprise tools via APIs.
• Configure and optimize RAG workflows and integrate with search/vector infra.
• Follow CI/CD, testing, and observability practices; own features end-to-end.
• Write technical docs, runbooks, and support UAT activities.

Connaissances

Agent/Agentic framework

Production AI systems

Python

Java/Node.js/React

Cloud AI stacks

Data modeling

Formation

Bachelor's degree + 8 years experience

Outils

LangChain

LangGraph

Vector stores

ServiceNow

Description du poste

Stanford University recruiting an experienced AI/GenAI engineer to design, implement, and support AI solutions across university use cases. The role blends hands-on engineering with mentorship, prototyping and productionizing proofs of concept, and partnering with project managers, security, and infrastructure teams.

You will lead AI tracks, develop LLM-based agents, and improve workflows with new capabilities (LLMs, RAG, MLOps) while ensuring governance and security.