As a member of Stanford University’s Enterprise Technology team, you will shape the next generation of AI capabilities across campus use cases, translating complex needs into reliable, scalable AI and GenAI solutions. This onsite role in Redwood City places you at the intersection of engineering excellence and university-scale impact, with opportunities to lead AI tracks and mentor junior engineers as you deliver impactful systems.
Overview
Stanford seeks an AI Engineer to design, implement, and support enterprise AI and GenAI solutions across a broad set of university applications. The role may serve as a technical lead for AI tracks and involves mentoring junior engineers while ensuring robust, secure, and observable deployments.
Responsibilities
- Translate requirements into engineered AI/ML components, including pipelines, vector stores, prompt and agent logic, and evaluation hooks, in collaboration with the platform and architecture team.
- Develop and maintain LLM-based agents and services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks; create lightweight internal SDKs or utilities as needed.
- Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search and vector infrastructure, escalating architecture changes to designated architects.
- Follow and refine SDLC practices for CI/CD, testing, prompt/model versioning, and observability; shepherd feature delivery through development, testing, and production with release coordination.
- Apply guardrails for governance, security, and compliance, collaborating with InfoSec and architects to mitigate gaps; document decisions and risks.
- Instrument services with KPIs (latency, cost, accuracy) and build lightweight dashboards; deep BI/reporting is not the primary focus.
- Write clear technical documentation, including APIs, workflows, runbooks, user stories, and acceptance criteria; support and occasionally lead UAT and testing activities.
- Lead collaborative sessions with stakeholders and mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags.
Requirements
- Bachelor’s degree and eight years of relevant experience, or an equivalent combination of education and experience.
- Agent/Agentic Framework Experience: built and shipped at least one production LLM agent or agentic workflow using frameworks such as LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents, or equivalent; able to justify tool choices and post-deployment support.
- Proven Delivery: three or more AI/ML projects and two or more GenAI/LLM projects in production with ongoing operational support and measurable efficiency gains.
- Strong foundation in AI/ML concepts (LLMs, transformers, classical ML) and experience designing, developing, testing, and deploying AI-driven applications.
- Programming Proficiency: Python as the primary language; experience with Node.js/Next.js/React/TypeScript and Java; quick learner of new tools and frameworks.
- Cloud AI stacks and vector/search tech: Google Vertex AI, AWS Bedrock, Azure OpenAI; vector databases and search technologies such as Pinecone, Elastic/OpenSearch, FAISS, Milvus, among others.
- Data architecture knowledge, relational and NoSQL databases, and data modeling.
- Solid understanding of SDLC, MLOps, and quality control practices.
- Strong problem-solving and systematic troubleshooting skills; ability to define and solve problems for highly technical applications.
- Excellent communication, listening, negotiation, and conflict resolution abilities; able to bridge functional and technical teams.
- Certifications: Google/AWS/Azure ML/AI certifications or a strong demonstrable portfolio of production AI systems.
Technologies
- LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents
- Python, Node.js, Next.js, React, TypeScript, Java
- Google Vertex AI, AWS Bedrock, Azure OpenAI
- Pinecone, Elastic/OpenSearch, FAISS, Milvus
- LangSmith, PromptLayer, Weights & Biases, LlamaIndex, DSPy, Haystack
- Agent Engine, Google ADK, AWS AgentCore
- Llama/Mistral/Qwen, vLLM/TGI/Ollama
- Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters
- BM25+dense, Cohere, Voyage, Jina
- ServiceNow, Salesforce, Oracle Financials
- Tailwind, Vertex Pipelines, MLflow, Kubeflow, SageMaker Pipelines
Benefits
- Career development programs
- Tuition reimbursement
- Audit a course
- Retirement plans
- Generous time-off and family care resources
- Health care benefits and health/fitness classes
- Free commuter programs and ridesharing incentives
- Discounts and access to campus sculptures, trails, and museums
Certifications and Licenses
Required: One of Google/AWS/Azure ML/AI certifications or a strong demonstrable portfolio of production AI systems.
Education & Experience
Bachelor's degree and eight years of relevant experience, or an equivalent combination of education and experience.
Physical Requirements
- Constantly perform desk-based computer tasks
- Frequently sit, grasp lightly, and perform fine manipulation
- Occasionally stand or walk and write by hand
- Rarely use a telephone; able to lift/carry/push/pull objects up to 10 pounds
Working Conditions
- May work extended hours, evenings, and weekends
Work Standards
- Interpersonal Skills: ability to collaborate with Stanford colleagues, clients, and external organizations
- Promote Culture of Safety: commitment to personal responsibility, safety training, and adherence to university policies
Why Stanford is for You
Stanford offers a culture that supports growth through career development programs and tuition reimbursement, a generous retirement plan, and rich campus resources. The environment emphasizes safety, health, and work-life balance, with access to health benefits plus on-campus amenities and experiences that contribute to a well-rounded professional life.
