# Machine Learning Engineer Resume Template

ML engineering resumes need both sides: the model and the system around it. Show what you trained or fine tuned, how it was served, and the latency, cost or quality numbers that followed.

_Source: Astra (https://useastra.in/resume-templates/machine-learning-engineer). Updated 2026-10-12. Level: Early career._

Use this template free: https://useastra.in/resume-builder/new?template=modern&role=machine-learning-engineer

## Skills to put on a Machine Learning Engineer resume

- **ML:** PyTorch, TensorFlow, Transformers, LLMs, RAG
- **Serving:** FastAPI, Docker, Kubernetes, ONNX, vLLM
- **Data and MLOps:** Python, SQL, Spark, MLflow, Vector Databases

## Weak vs strong resume bullets

- Weak: Worked on an LLM chatbot.
  Strong: Built a retrieval augmented support assistant over 12,000 help articles; it resolved 34% of tickets without an agent.
- Weak: Optimised model inference.
  Strong: Quantised and moved the ranking model to ONNX Runtime, cutting p95 inference from 140 ms to 45 ms.

## Tips for a Machine Learning Engineer resume

- Show the serving side: latency, throughput, cost per request.
- For LLM work, say how you measured quality (eval sets, human review, resolution rate).
- Name the frameworks exactly as job posts do: PyTorch, Hugging Face Transformers, vLLM.

## Example resume

Fictional person and employers. Numbers are illustrative.

**Siddharth Menon**, Machine Learning Engineer, Bengaluru, India

ML engineer with 3 years shipping ranking and LLM systems. Built a support assistant that resolves 34% of tickets and cut ranking inference latency by 68%.

### Experience

**Machine Learning Engineer, Helpstack** (Bengaluru, Feb 2024 to Present)
- Built a RAG support assistant over 12,000 articles; 34% of tickets resolved without an agent.
- Set up an eval set of 600 real questions, run on every prompt or model change.

**ML Engineer, Feedly Media** (Bengaluru, Jul 2022 to Jan 2024)
- Moved the ranking model to ONNX Runtime with quantisation; p95 fell from 140 ms to 45 ms.
- Built the Spark feature pipeline feeding daily model retrains.

### Education

- B.Tech Computer Science, National Institute of Technology Calicut, 2018 to 2022, CGPA 8.6

### Skills

- ML: PyTorch, Transformers, RAG, LLM Evaluation
- Serving: FastAPI, ONNX Runtime, Docker, Kubernetes
- Data: Python, SQL, Spark, pgvector

## Frequently asked questions

### How is an ML engineer resume different from a data scientist resume?

An ML engineer resume puts more weight on production: serving, scaling, latency and pipelines. A data scientist resume leans on analysis, modelling and business impact.

### Should I add LLM projects to my resume?

Yes, if you can explain how you evaluated them. A small RAG project with a clear eval beats a long list of API wrappers.

## Related

- Data Scientist resume template: https://useastra.in/resume-templates/data-scientist
- Backend Developer resume template: https://useastra.in/resume-templates/backend-developer
- Software Engineer resume template: https://useastra.in/resume-templates/software-engineer
- AI/ML jobs in India: https://useastra.in/jobs/category/ai-ml
- Free ATS resume checker: https://useastra.in/free-tools/ats-resume-checker