Why This Resume Works
12K requests per second, 5M+ users, and 2M+ documents processed. This is production engineering, not research prototyping.
72% to 91% accuracy on domain-specific tasks quantifies the value of custom fine-tuning expertise.
65% API cost reduction shows business awareness. Engineers who save money while building features get hired faster.
Section-by-Section Breakdown
Summary
Lead with user scale, throughput metrics, and your core technical specialties. GenAI roles demand proof of production deployment.
Skills
List Models, Frameworks, Infrastructure, and Techniques separately. This field moves fast, so name specific tools and versions.
Experience
Every bullet needs a scale metric (requests/second, documents, users) and a quality metric (accuracy, latency, cost savings).
Education
ML-focused CS degrees from strong programs carry weight. Mention specific research areas or publications if relevant.
Key Skills for Generative AI Engineer Resumes
Based on analysis of thousands of job postings, these are the most frequently required skills:
Common Mistakes on Generative AI Engineer Resumes
- ⚠Listing only API wrapper experience - Calling OpenAI's API is not engineering. Show fine-tuning, RAG architecture, inference optimization, or custom model work.
- ⚠No latency or throughput metrics - GenAI engineering is defined by performance at scale. p99 latency and requests/second are essential resume metrics.
- ⚠Missing cost optimization work - LLM costs are a top concern for every company. Showing 65% cost reduction is as valuable as showing accuracy gains.
- ⚠Not specifying which models you fine-tuned - Fine-tuned an LLM is vague. Name the base model, dataset size, technique (LoRA/QLoRA), and resulting accuracy improvement.
- ⚠Omitting infrastructure and deployment details - GenAI engineers own the full stack from training to serving. Kubernetes, vLLM, and SageMaker experience must be visible.