AI Data Engineer

Job Purpose

Are you an experienced AI/GenAI engineer who loves shipping real systems with a passion for working with enterprise data? Join Stanford’s Enterprise Technology team to design, implement, and support AI solutions across university use cases. In this role, you will influence strategic direction, requirements, and architecture for AI‑driven information systems, incorporating new capabilities (LLMs, RAG, agentic frameworks, MLOps) to improve workflow, efficiency, and decision‑making.

You may serve as the technical lead for specific AI tracks and interrelated applications.

This role blends hands‑on engineering with mentorship and thought leadership. You will prototype and product ionize—presenting proofs of concept, demoing solutions to stakeholders, and partnering with project managers, technical managers, architects, security, infrastructure, and application teams (Service Now, Salesforce, Oracle Financials, etc.)

• 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 (Service Now, 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 Info Sec 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.
• Documentation & Communication:
  Write clear technical docs (APIs, workflows, runbooks), 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.

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 Lang Graph, Lang Chain, CrewAI/Auto Gen, 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 (Service Now, 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 Type Script).
• Experience with cloud…