Junior AI Applications Engineer

About the role

This is a 1-year, Fixed-Term Position. Join Stanford’s Enterprise Technology team to design, implement, and support AI solutions across university use cases.

Responsibilities

• Assess user needs and requirements.
• Design and develop applications that may involve sophisticated data manipulation.
• Maintain and update existing programs.
• Troubleshoot and solve technical problems.
• Create programs to meet reporting and analysis needs.
• Design and implement user and operations training programs.
• Document changes in software for end users.
• Follow team software development methodology.
• Serve as a technical resource with respect to applications.

Requirements

Desired Knowledge, Skills, And Abilities:

• Agent Interoperability Protocols: Familiarity with agent-to-agent coordination standards such as A2A (Agent2Agent) for multi-agent workflows; awareness that production systems increasingly run MCP (agent-to-tool) and A2A (agent-to-agent) together.
• Agentic Evaluation: Setting up evaluation frameworks for agents: LLM-as-a-Judge for reasoning/task-completion quality, plus tracking accuracy, latency, and cost across agent runs (rubrics, hallucination/bias checks, A/B tests, golden sets).
• Deployment & Infrastructure: Docker and Kubernetes, CI/CD pipelines, and microservices architecture for serving modular, independently scalable agent components.
• AI-Assisted Development: Productive use of AI coding assistants (e.g., Claude Code, Cursor, GitHub Copilot) in day-to-day engineering.
• 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.
• 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.

Qualifications

• Bachelor's degree and three years of relevant experience or a combination of education and relevant experience.
• Working knowledge of latest software and design standards.
• Ability to define and solve logical problems for technical applications.
• Knowledge of and ability to select, adapt, and effectively use a variety of programming methods.
• Ability to recognize and recommend needed changes in user and/or operations procedures.
• Strong knowledge of at least one programming language.