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Inside Higher Ed · 2 days ago

AI Applications Engineer

Stanford, CA

Full-time

Hybrid

Senior Level, Lead/Staff

$170K/yr - $190K/yr

8+ years exp

Stanford University is seeking an experienced AI Applications Engineer to join their Enterprise Technology team. The role involves designing, implementing, and supporting AI solutions to enhance workflow efficiency and decision-making across university use cases.

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Responsibilities

Translate requirements into well-engineered components (pipelines, vector stores, prompt/agent logic, evaluation hooks) and implement them in partnership with the platform/architecture team

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

Configure and optimize RAG workflows (chunking, embeddings, metadata filters) and integrate with existing search/vector infrastructure—escalating architecture changes to designated architects

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

Apply established guardrails (PII redaction, policy checks, access controls). Partner with InfoSec and architects to close gaps; document decisions and risks

Instrument services with KPIs (latency, cost, accuracy/quality) and build lightweight dashboards. Deep BI/reporting not primary

Write clear technical docs (APIs, workflows, runbooks), user stories, and acceptance criteria. Support and sometimes lead UAT/test activities

Facilitate working sessions with stakeholders; mentor junior engineers through code reviews and pair programming; provide concise updates and risk flags

Qualification

AI/GenAI EngineeringLLM Agent DevelopmentMLOps PracticesProgramming ExpertiseCloud AI StacksData Design/ArchitectureSDLC UnderstandingOpen Source ExperienceProblem-Solving SkillsCommunication SkillsCollaboration SkillsMentorship Skills

Required

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 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

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

Preferred

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, and 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

One of (or equivalent experience with): Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems