AI Engineer

About the role

Join Stanford’s Enterprise Technology team to design, implement, and support AI solutions across university use cases. Influence strategic direction, requirements, and architecture for AI-driven information systems, incorporating new capabilities like LLMs, RAG, agentic frameworks, and MLOps.

Responsibilities

• AI/ML System Implementation & Integration: Translate requirements into well-engineered components and implement them in partnership with the platform/architecture team.
• Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools using approved APIs and tool-calling frameworks.
• RAG & Search Enablement: Configure and optimize RAG workflows and integrate with existing search/vector infrastructure, escalating architecture changes to designated architects.
• MLOps & SDLC Practices: Follow and improve team standards for CI/CD, testing, prompt/model versioning, and observability. Own feature delivery through dev/test/prod, coordinating with release managers.
• Governance, Security & Compliance: Apply established guardrails, partner with InfoSec and architects to close gaps; document decisions and risks.
• Metrics & Reporting: Instrument services with KPIs and build lightweight dashboards.
• Documentation & Communication: Write clear technical docs, user stories, and acceptance criteria. Support and sometimes lead UAT/test activities.
• Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags.

Qualifications

• Bachelor's degree and eight years 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 like LangGraph, LangChain, CrewAI/AutoGen, Google Agent Builder/Vertex AI Agents (or equivalent).
• Proven Delivery: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response).
• Strong understanding of AI/ML concepts (LLMs/transformers and classical ML) and experience designing, developing, testing, and deploying AI-driven applications.
• Programming Expertise: Python (primary) plus experience with Node.js/Next.js/React/TypeScript and Java; demonstrated ability to quickly learn new tools/frameworks.
• Experience with cloud AI stacks (e.g., 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.
• Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills.
• Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources.

Desired Knowledge, Skills, and Abilities

• MLOps Tooling: MLflow, Kubeflow, Vertex Pipelines, SageMaker Pipelines; LangSmith/PromptLayer/Weights & Biases.
• Open Source Savvy: Experience working with, customizing, and improving open-source solutions; comfortable contributing fixes/features upstream.
• Rapid Tech Adoption: Demonstrated ability to pick up a new technology/framework quickly and deliver production value with it.
• GenAI Frameworks: LangChain, LlamaIndex, DSPy, Haystack, LangGraph, Agent Engine, Google ADK, AWS AgentCore, CrewAI/AutoGen.
• Security & Governance: Implementing AI guardrails, red-teaming, policy enforcement frameworks.
• Enterprise Integrations: ServiceNow, Salesforce, Oracle Financials or others.
• UI Development: React/Next.js/Tailwind for internal tools.
• Prompt engineering at scale: Structured prompts (JSON/function-calling), templates, version control; automated/offline & online evals (rubrics, hallucination/bias checks, A/B tests, golden sets).
• Parameter-efficient fine-tuning (LoRA/QLoRA/adapters), supervised instruction tuning; hosting open-weight models (Llama/Mistral/Qwen) with vLLM/TGI/Ollama.
• Safety/guardrails frameworks (Guardrails.ai, NeMo Guardrails, Azure/AWS safety filters) and jailbreak/drift detection.
• Hybrid search & reranking (BM25+dense, Cohere/Voyage/Jina rerankers), synthetic data generation, provenance/watermarking.
• Telemetry & governance: prompt/model drift monitoring, policy-as-code, audit logging, red-teaming playbooks.

Benefits

The expected pay range for this position is $169,728 to $194,585 per annum. Stanford University provides a comprehensive rewards package, including:

• Retirement plans
• Tuition reimbursement
• Health care benefits
• Commuter programs
• Ridesharing incentives
• Discounts

Stanford University is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law.