A complete, annotated resume for an AI engineer shipping LLM-powered features to production. Every section is broken down — so you can see exactly what makes this resume land interviews.
Scroll down to see the full resume, then read why each section works.
AI engineer with 3+ years of experience building LLM-powered features and retrieval-augmented generation pipelines for production applications. Currently shipping AI features at Notion, where I reduced hallucination rates by 40% across the Q&A product through a custom RAG pipeline serving 50K+ daily queries at sub-2s latency. Previously built the ML platform at a Series B startup from zero to production-ready model serving.
Languages: Python, TypeScript, SQL ML Frameworks: PyTorch, Hugging Face Transformers, LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Chroma, pgvector Infrastructure: AWS SageMaker, Docker, Kubernetes, Airflow Evaluation: MLflow, Weights & Biases, custom LLM-as-judge frameworks
Seven things this AI engineer resume does that most don’t.
Instead of “machine learning engineer with experience in AI,” Priya’s summary names exact systems: LLM-powered features, RAG pipelines, production model serving. It immediately tells a hiring manager this person works at the application layer of AI — building things users interact with — not just training models in notebooks.
Latency, hallucination rate, retrieval accuracy, cost per query, feature freshness — these are the metrics AI engineering teams actually care about. Generic metrics like “improved performance” don’t work here. Priya’s bullets prove she understands what to measure and how to move the numbers that matter in production AI systems.
This resume doesn’t lead with papers published or models benchmarked. It leads with systems shipped to production: a RAG pipeline handling 50K queries/day, a model serving infrastructure with automatic rollback, a feature pipeline processing 3M events/day. For AI engineering roles, proving you can ship is more valuable than proving you can research.
Notice how Priya doesn’t say “used LangChain to build a chatbot.” She describes the architectural decisions: hybrid search with dense and sparse retrieval, context-aware chunking using document structure signals, semantic caching with embedding similarity. This signals an engineer who designs systems, not someone who strings API calls together.
An open-source RAG toolkit with 1,200+ stars and 40+ companies using it in production isn’t just a side project — it’s proof that Priya understands the problem space deeply enough to build reusable tools for it. Interviewers can look at the code, read the docs, and see real engineering decisions. This is the hardest signal to fake on a resume.
Not a flat list of every ML tool. Organized into Languages, ML Frameworks, Vector Databases, Infrastructure, and Evaluation — mirroring the layers of a production AI system. A hiring manager can see at a glance that Priya covers the full stack from model training to serving to monitoring, and knows exactly which tools she uses at each layer.
The MS in Computer Science with an ML focus from Georgia Tech establishes credibility in two lines. No coursework lists, no GPA, no thesis title. The specialization is clear from the degree name, and everything above it — the production work, the open-source project, the quantified impact — already proves technical depth far more convincingly than any transcript could.
The weak version describes a tutorial project. The strong version shows production scale, specific quality metrics, and the tradeoff between accuracy and latency that real AI engineering requires.
The weak version is generic excitement. The strong version names the exact type of AI work, the production context, and a specific result — in two sentences.
The weak version is a buzzword dump that lists concepts (Deep Learning, AI) alongside tools. The strong version is categorized by stack layer, only includes tools actually used in production, and shows a coherent technical worldview.
This exact resume template helped our founder land a remote data scientist role — beating 2,000+ other applicants, with zero connections and zero referrals. Just a great resume, tailored to the job.
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