Stanford University seeks to design, implement, and support AI solutions across enterprise use cases to improve workflow, efficiency, and decision-making. The role involves influencing strategic direction and architecture for AI-driven information systems, incorporating new capabilities such as LLMs, RAG, agentic frameworks, and MLOps.
Requirements
- 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: Implemented 3+ AI/ML projects and 2+ GenAI/LLM projects in production, with operational support (monitoring, tuning, incident response). Projects should serve sizable user populations and demonstrate measurable efficiency gains.
- Enterprise Data Understanding: Strong knowledge of enterprise systems (ServiceNow, Salesforce, Oracle Financials, etc.) and how to extract, transform, and analyze data from them.
- Data Engineering & Analysis: Proficiency in building data pipelines, conducting exploratory data analysis (EDA), profiling datasets, and preparing features for ML/AI use cases.
- 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), with experience in SQL and one or more general-purpose languages (Java, Node.js, or TypeScript).
- Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.).
Responsibilities
- AI/ML System Implementation & Integration: Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team.
- Data Engineering & EDA: Build and optimize data ingestion, transformation, and quality pipelines. Conduct exploratory data analysis (EDA) to surface patterns, anomalies, and insights that inform AI models and decision-making.
- Application & Agent Development: Build and maintain LLM-based agents/services that securely call enterprise tools (ServiceNow, Salesforce, Oracle, etc.) using approved APIs and tool-calling frameworks. Create lightweight internal SDKs/utilities where needed.
- RAG & Search Enablement: Configure and optimize RAG workflows (chunking, embeddings, metadata filters) 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 (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks.
- Metrics & Reporting: Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting.
Other
- Bachelor's degree and eight years of relevant experience or a combination of education and relevant experience.
- Excellent communication, listening, negotiation, and conflict resolution skills; ability to bridge functional and technical resources.
- Ability to define/solve logical problems for highly technical applications; strong problem-solving and systematic troubleshooting skills.
- Collaboration & Mentorship: Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags.
- Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
