ML engineer · MLOps · LLMs

Machine Learning Engineer

Template for ML engineers who need to prove production shipping: serving, MLOps, observability and inference cost.

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Diego Navarro

MACHINE LEARNING ENGINEER

[email protected]+1 555 444 4444Valencia, Spainlinkedin.com/in/diegonavarrogithub.com/diegonavarro

Skills

Languages: Python, Go, Rust, SQL, C++ (basic CUDA kernels)

ML frameworks: PyTorch, JAX, Transformers, PyTorch Lightning, scikit-learn

Serving & inference: Triton, vLLM, TorchServe, KServe, ONNX Runtime, TensorRT

MLOps: Kubeflow, Argo Workflows, MLflow, Weights & Biases, DVC, BentoML

Experience

Lumina AI

Senior Machine Learning Engineer · Remote · Jan 2023 – Present

· Designed and shipped a RAG system over 4M internal documents serving 80 req/s with p95 latency of 850 ms (vLLM…

· Cut LLM inference cost by 62% combining quantization (AWQ), dynamic batching and small-model routing for trivi…

Northbeam Logistics

Machine Learning Engineer · Madrid · Jun 2020 – Dec 2022

· Shipped a fraud-detection model (gradient boosting + real-time features with Feast) that recovered €1.2M/year …

· Built the streaming feature pipeline on Kafka + Flink, serving 4,500 real-time features with p99 latency < 30 …

Projects

tinyserve (Rust, ONNX) · https://github.com/diegonavarro/tinyserve

llm-evals-template · https://github.com/diegonavarro/llm-evals-template

Education

Universidad Politécnica de Valencia

M.Sc. in Artificial Intelligence, Pattern Recognition and Digital Imaging · 2018 – 2020

Universidad Politécnica de Valencia

B.Sc. in Computer Engineering · 2014 – 2018

Resume for machine-learning engineers focused on production: serving, MLOps, evals and model observability. Aimed at profiles shipping models (including LLMs) to production, not pure research.

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