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AI Applications Engineer

Job in Stanford, Santa Clara County, California, 94305, USA
Listing for: Inside Higher Ed
Full Time position
Listed on 2025-12-19
Job specializations:
  • IT/Tech
    AI Engineer
Job Description & How to Apply Below

Join to apply for the AI Applications Engineer role at Inside Higher Ed

šŸ”Business Affairs:
University IT (UIT), Redwood City, California, United States šŸ“Information Technology Services šŸ“…Sep 08, 2025 Post Date

Job Purpose

Are you an experienced AI/GenAI engineer who loves shipping real systems? 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.).

Core Duties
  • 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.
  • 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 not primary.
  • 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.
Education & Experience

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

Required Knowledge, Skills, And Abilities
  • 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.
  • 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/Type Script 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/Open Search, FAISS, Milvus, etc.).
  • Knowledge of data design/architecture, relational and No

    SQL databases, and data modeling.
  • Thorough understanding of SDLC, MLOps, and quality control practices.
  • Ability to define/solve logical problems for highly technical applications;…
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