Resume Guide
A strong resume should prove capability, clarity, and business impact in less than a minute of reading time.
Recommended structure
- Headline: role focus + years of experience + domain strengths.
- Experience: achievements with concrete metrics and responsibilities.
- Projects: architecture, constraints, tools, and measurable outcomes.
- Skills: grouped by practical usage, not alphabetical keyword dumping.
What to show for GenAI and Agentic AI roles
- Evaluation quality signals such as accuracy, relevance, or task completion.
- Latency and cost trade-offs you made and why they mattered.
- Reliability and safety controls for agent behavior and tool calls.
ATS and recruiter readability
- Use clean section headers and standard role titles.
- Tailor core keywords based on the target role requirements.
- Avoid dense paragraphs; use concise bullet points with outcomes.
Common mistakes to avoid
- Listing too many tools without showing what was built with them.
- Using vague impact statements with no numeric evidence.
- Submitting the same resume to every role without tailoring.
GenAI Resume Checklist
Before submitting your resume for any AI/ML role, run through these eight checks:
- 1Model sizes and context windows you have worked with are mentioned (e.g., 7B, 70B, 128k context).
- 2Evaluation metrics are cited — RAGAS scores, accuracy delta vs baseline, or latency P95.
- 3RAG pipeline details are specific: embedding model, vector store, chunk size, re-ranker used.
- 4Latency and cost optimisation work is quantified — e.g., '40% cost reduction via LiteLLM routing'.
- 5Demo links, GitHub repos, or Hugging Face Spaces are live and load correctly.
- 6Open-source contributions are listed with repo name, PR type, and merge status.
- 7Hugging Face profile link is included if you have uploaded any models or datasets.
- 8Top 3 GitHub repos have proper READMEs with architecture diagrams and benchmark results.