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Job Summary:
Stanford University is seeking an experienced AI Engineer to join their Enterprise Technology team. The role involves designing, implementing, and supporting AI solutions, influencing strategic direction for AI-driven information systems, and blending hands-on engineering with mentorship and thought leadership.
Responsibilities:
• 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 (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: 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 InfoSec 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.
Qualifications:
Required:
• Bachelor's degree and eight years 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.
• Required: One of (or equivalent experience with): Google/AWS/Azure ML/AI certifications or strong demonstrable portfolio of production AI systems.
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 is a place of discovery, creativity and innovation located in the San Francisco Bay Area on the ancestral land of the Muwekma Ohlone Tribe. Founded in 2008, the company is headquartered in Stanford, USA, with a team of 10001+ employees. The company is currently Late Stage.