AI Engineer

Stanford University's Enterprise Technology team is seeking an AI Engineer to design, implement, and support AI and GenAI solutions across university use cases. This onsite role in Redwood City, CA may lead AI tracks and includes mentoring junior engineers as part of a collaborative, impact-focused environment.

Compensation

Salary: USD 169,728 - 194,585 per year

Responsibilities

• Translate business requirements into robust AI/ML system components such as data pipelines, vector stores, prompt and agent logic, and evaluation hooks, working with the platform and architecture teams.
• Develop and maintain LLM-based agents and services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) through approved APIs and tool-calling frameworks; create lightweight internal SDKs or utilities as needed.
• Configure and optimize retrieval-augmented generation workflows, including chunking, embeddings, and metadata filters; integrate with existing search and vector infrastructure and elevate architectural considerations to designated architects.
• Adhere to and enhance CI/CD, testing, prompt/model versioning, and observability practices; shepherd feature delivery from development through production with coordination from release managers.
• Apply guardrails for governance, security, and compliance (PII redaction, policy checks, access controls); partner with InfoSec and architects to address gaps and document decisions and risks.
• Instrument services with KPIs such as latency, cost, and accuracy, and build lightweight dashboards to monitor performance; deep BI/reporting is not the primary focus.
• Produce clear technical documentation (APIs, workflows, runbooks), write user stories and acceptance criteria, and support or lead UAT/testing activities.
• Lead stakeholder sessions, mentor junior engineers through code reviews and pair programming, and provide concise updates and risk flags.

Requirements

• Bachelor's degree and eight years of relevant experience, or a combination of education and relevant 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 explain tool selection, orchestration logic, and post-deployment support.
• Proven delivery: completed 3+ AI/ML projects and 2+ GenAI/LLM projects in production with ongoing operational support, serving sizable user populations and demonstrating measurable efficiency gains.
• Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) with experience designing, developing, testing, and deploying AI-driven applications.
• Programming expertise: Python as the primary language, plus experience with Node.js/Next.js/React/TypeScript and Java; demonstrated ability to quickly learn new tools and frameworks.
• Experience with cloud AI stacks (Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.).
• Knowledge of data design/architecture, relational and NoSQL databases, and data modeling.
• Thorough understanding of SDLC, MLOps, and quality control practices.
• Proven problem-solving and systematic troubleshooting skills; ability to define and solve complex technical problems.
• Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources.
• Certifications: One of (or equivalent experience with) 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
• Family care resources
• Rock climbing facilities
• Health care benefits
• Health/fitness classes
• Free commuter programs
• Ridesharing incentives
• Discounts
• Access to sculptures, trails, and museums

Certifications & Licenses

• Required: One of (or equivalent experience with): Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems.

Education & Experience

Bachelor's degree and eight years relevant experience, or a combination of education and relevant experience.

Physical Requirements

• Constantly perform desk-based computer tasks
• Frequently sit, grasp lightly and perform fine manipulation
• Occasionally stand or walk, write by hand
• Rarely lift or carry objects up to 10 pounds

Working Conditions

May work extended hours, evenings, and weekends.

Work Standards

• Interpersonal skills: ability to collaborate effectively with Stanford colleagues, clients, and external organizations
• Promote culture of safety: uphold safety responsibilities, raise safety concerns, and follow training and procedures; align with university policies and procedures