AI Engineer Resume

Shipped LLM applications, not experiments

The AI space is full of people who've 'worked with GPT-4' or 'experimented with LangChain.' What differentiates AI engineers who get callbacks is evidence of shipped production applications: customer-facing features using LLMs, internal tools with real usage, or projects with measurable business impact. Specify the model (GPT-4o, Claude 3.5, Llama 3), the framework (LangChain, LlamaIndex), the storage layer (Pinecone, pgvector, Weaviate), and what it does for users at scale.

RAG architecture and evaluation

Retrieval-Augmented Generation is the core pattern for most enterprise AI applications in 2025. Resume evidence of building a real RAG pipeline — chunking strategy, embedding choice, retrieval optimization, re-ranking, and especially evaluation using RAGAS, TruLens, or a custom eval framework — signals hands-on production experience. 'Built RAG system' is common. 'Built RAG system with hybrid BM25+dense retrieval, evaluated with RAGAS, achieving 0.87 faithfulness score across 500-question benchmark' is rare and compelling.

Fine-tuning and model customization

Fine-tuning experience (LoRA, QLoRA, full fine-tune) is increasingly asked for at senior AI engineer levels. Show the base model, the method, the dataset, and what improved: 'Fine-tuned Llama 3.1 8B using QLoRA on 15K proprietary support conversations — 31% reduction in hallucination rate vs zero-shot GPT-4o on domain-specific benchmark.' This combination of choices made + outcome measured is a senior signal.

Prompt engineering as engineering, not art

Senior AI engineers treat prompt engineering systematically: version-controlled prompts, A/B tested prompt variants, systematic eval frameworks, and documented performance trade-offs. Resume language that shows this discipline — 'Developed versioned prompt library with automated regression testing across 200 test cases' — distinguishes you from candidates who think prompt engineering is just iterating in a chat window.