Junior AI Applications Engineer

Job Purpose

Are you an 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'll work hands-on to implement LLM/RAG services, integrate with enterprise platforms (Service Now, Salesforce, Oracle Financials, etc.), and follow strong MLOps/SDLC practices. You'll prototype, harden, and ship features-partnering closely with product, security, infrastructure, and application teams.

This is an applied engineering role (not research). You'll learn rapidly, contribute code daily, write clear docs, and develop strong habits in quality, governance, and cost/latency optimization.

Core Duties

* 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 (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:
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.

Education & Experience:

Bachelor's degree and three 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 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.

* Programming Expertise:
Proficient in Python; familiarity with Node.js/Type Script/React and RESTful APIs; ability to read/extend existing codebases.

* Vector & Search Basics:
Worked with at least one vector/search tech (e.g., Pinecone, Open Search/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/Open Search, 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…