AI Engineer

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.