Junior AI Applications Engineer

Job Summary: Stanford University is seeking a Junior AI Applications Engineer to join its Enterprise Technology team. This role involves designing, implementing, and supporting AI solutions for various university use cases, focusing on hands-on implementation of LLM/RAG services and integration with enterprise platforms. Responsibilities: • AI/ML System Implementation & Integration: Assess user needs and requirements, • Turn requirements and tickets into well-engineered components (data prep, pipelines, vector stores, prompts/agents, evaluation hooks). • Application & Agent Development: Build, maintain, and update programs like 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: Contribute tests, CI/CD pipelines, telemetry, and prompt/model versioning; participate in code reviews and release activities across dev/test/prod; follow team software development methodology. • Governance, Security & Compliance: Apply established guardrails (PII redaction, policy checks, access controls/minimum-privilege). Document decisions and known risks. • Metrics & Reporting: Create programs to meet reporting and analysis needs; 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, user stories, and acceptance criteria; design and implement user and operations training programs; document changes in software for end users. Support and sometimes lead UAT/test activities. • Collaboration & Mentorship: Participate in working sessions with stakeholders; receive and give code review feedback; pair program with senior engineers; proactively upskill on platforms and frameworks. Qualifications: Required: • Bachelors degree and three years of relevant experience or a combination of education and relevant 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. • Implemented 1+ AI/ML projects and 1+ 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. • Proficient in Python; familiarity with Node.js/TypeScript/React and RESTful APIs; ability to read/extend existing codebases. • Worked with at least one vector/search tech (e.g., Pinecone, OpenSearch/Elasticsearch, FAISS, Milvus) and basic embedding workflows. • Experience with cloud AI stacks (e.g., Google Vertex AI, AWS Bedrock, Azure OpenAI) and vector/search technologies (Pinecone, Elastic/OpenSearch, FAISS, Milvus, etc.). • Thorough understanding of SDLC, MLOps, and quality control practices. • Ability to define/solve logical & technical 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, 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. Company: Stanford University is a teaching and research university that focuses on graduate programs in law, medicine, education, and business. Founded in 1885, the company is headquartered in Stanford, USA, with a team of 10001+ employees. The company is currently Late Stage.

Created: 2026-03-05