A template for ML engineers who build intelligent systems that learn and scale.
Machine Learning Engineer with 5 years of experience building and deploying production ML systems. Expert in LLM fine-tuning, computer vision, and MLOps. Experience at both start-ups and FAANG-scale companies.
ML Engineer CVs must show production deployment experience, not just research. Recruiters want to see models you deployed at scale, the infrastructure you built, and measurable business outcomes. Publications and open-source contributions add credibility.
PyTorch, TensorFlow, LLM fine-tuning, MLOps (Kubeflow, MLflow), Python, C++, computer vision, NLP, cloud platforms (AWS/GCP), and distributed training.
Listing every ML technique without showing real-world application. Focus on 3 to 5 production systems you built and their business impact. Show latency, throughput, and cost optimisation achievements.
One to two pages. Include a Selected Projects section if your work spans multiple domains. Link to publications, GitHub, or demo projects prominently.
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