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AI Engineer CV Example

Professional ai engineer CV example with recruiter-tested sections you can customise in minutes.

Example only

All personal details and career history in this example are fictional. Customise this CV with your own real information before applying.

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Skills

Python
PyTorch
LangChain
LLMs
Vector Databases
RAG Systems
FastAPI
Docker
Prompt Engineering
OpenAI APIs

Certifications

Microsoft Certified: Azure AI Engineer Associate
Microsoft
2023-06-01
Google Cloud Generative AI Engineer Path
Google
2022-06-01

Languages

English (Native)
Mandarin Chinese (Conversational)

Chloe Morgan

AI Engineer
5 years of experience building generative AI products, retrieval systems, and LLM-powered business applications.

Experience

Senior AI Engineer
Cortex Weave, Full-time
2021-09-01 – Present
San Francisco, CA, US
LLM products with safety and evaluation — RAG with re-ranking, guardrail middleware, and automated eval suites.
- Designed LLM-powered products with robust safety and evaluation workflows - Implemented retrieval-augmented generation with vector re-ranking and source attribution, lifting answer accuracy in offline evals by 27%. - Built prompt versioning and guardrail middleware for policy, PII redaction, and jailbreak detection before model calls. - Defined automated eval suites with golden datasets and rubric scoring to gate releases of conversational agents.
AI Engineer
Relay Minds, Full-time
2020-08-01 – 2021-08-01
Berlin, Germany
AI copilots for operations and support — multi-model routing, streaming UX, and schema-validated tool calling.
- Developed AI copilots for internal operations and customer support - Integrated OpenAI and local fallback models behind a routing layer tuned for latency, cost, and quality thresholds. - Built streaming response UX with interruption handling and context window summarization for long chats. - Created tool-calling orchestration for CRM and billing APIs with strict JSON schema validation.
Junior AI Engineer
Syntax Orbit, Full-time
2019-07-01 – 2020-07-01
Austin, TX, US
Generative AI experimentation and deployment — prompt experiments, embedding-refresh jobs, and token/cost monitoring.
- Supported experimentation and deployment of generative AI features - Constructed prompt experiments for intent classification and response tone control using LangChain evaluators. - Implemented embedding refresh jobs with incremental document chunking to keep knowledge bases current. - Monitored token usage and request traces in Langfuse dashboards to detect cost regressions after feature launches.

Education

Artificial Intelligence, Master of Science
Stanford University
2019-09-01 – 2021-06-01
Graduate studies in artificial intelligence covering advanced theory, applied projects, and research methods. Coursework emphasized Python, PyTorch, LangChain and professional communication.

CV writing guide

  • Provide concrete examples of evaluation strategy, not just prompt engineering.
  • Show safety controls for harmful output, leakage, and hallucination mitigation.
  • Demonstrate end-to-end productization from prototype to monitored deployment.
  • Microsoft Certified: Azure AI Engineer Associate
  • Google Cloud Generative AI Engineer Path
  • DeepLearning.AI Generative AI with LLMs