AI Engineer Resume Guide
AI Engineer is the highest-growth title of the decade. The resume bar is also rising fast — hiring managers now expect concrete production AI work, not just notebook experiments. Lead with shipped systems, evals run, models served, and the latency / cost / quality trade-offs you made.
Resume Tips
- Lead with shipped AI systems and their business impact — users served, accuracy lift, cost reduced.
- Name the specific models, frameworks, and serving stack — GPT-4o, Claude, Llama, LangChain, vLLM, etc.
- Describe evaluation methodology — how you measured quality, not just what you built.
- Show the cost / latency / quality trade-offs you owned, especially for production deployments.
Recommended Keywords
Recommended Skills
How to Write Your Resume
Lead with shipped AI systems
Summary should name what you've shipped — "Shipped LLM-powered support agent serving 2M users with 92% deflection".
Name specific models and frameworks
GPT-4o, Claude Opus, Llama 3, LangChain, LlamaIndex, vLLM — recruiters filter on these literally.
Describe your evals
Show how you measured quality — golden datasets, LLM-as-judge, human eval rubrics, drift detection.
Show cost and latency trade-offs
Production AI is constrained by cost and latency. Show the trade-offs you made and what you saved.
Link to shipped artifacts
Live demos, GitHub repos, blog posts, conference talks — concrete artifacts beat vague claims of "worked with LLMs".
Resume Example
Arjun Mehta
AI Engineer | LLMs · RAG · Agents · Production GenAI
arjunmehta
arjunmehtaSummary
AI Engineer with 5 years building production LLM systems serving 4M+ monthly users. Shipped RAG pipelines for enterprise customer-support agents at 92% deflection rate. Deep experience with OpenAI, Anthropic, and self-hosted Llama 3 deployments. Owned evals, cost optimisation, and on-call for live AI services.
Experience
Architected RAG-based support agent on Claude 3.5 Sonnet serving 4.2M monthly conversations at 92% deflection and 87% CSAT.
Designed eval pipeline with golden datasets, LLM-as-judge rubrics, and weekly regression sweeps — reduced shipped-prompt errors by 73%.
Reduced inference cost 41% via prompt caching, tiered model routing (Haiku → Sonnet → Opus), and embedding-first retrieval gating.
Led migration from OpenAI to multi-provider (Anthropic + Bedrock) for vendor resilience; zero customer-facing outages in 14 months.
Built fraud-scoring model that reduced false positives 28% while holding fraud loss flat at $1.2M annualised.
Shipped real-time feature pipeline on Kafka + Flink serving 50k feature lookups/sec at p99 18ms.
Mentored 4 ML engineers through model deployment lifecycle and on-call rotations.
Projects
Built and open-sourced a RAG-eval framework with 2.4k GitHub stars. Used by 60+ teams across the GenAI ecosystem. Python, FastAPI, OpenAI/Anthropic SDKs.
Education
Thesis: Retrieval-augmented question answering over enterprise document corpora. Advisor: Prof. Tom Mitchell.
Skills
Certifications
Frequently Asked Questions
What separates an AI Engineer from a Machine Learning Engineer?
Do I need to have trained a foundation model to be hired?
How do I show prompt engineering work?
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