Resume guide for ML engineers: how to showcase production ML, MLOps, model optimization, and the engineering skills that separate ML engineers from data scientists.
These are the skills ATS systems scan for most heavily in Machine Learning Engineer job descriptions. Make sure you mention the ones you genuinely have — in your skills section AND woven into your experience bullets.
The most common mistake in Machine Learning Engineer resumes is writing vague duty descriptions instead of impact statements. Here's how to fix the most frequent offenders:
Deployed machine learning models.
Built ML serving infrastructure on Kubernetes handling 10K predictions/second with p99 latency <50ms, supporting 3 production models with zero-downtime updates.
Worked on model optimization.
Optimized BERT inference for production using ONNX quantization and TensorRT, reducing model size by 4x and inference latency by 65% with <1% accuracy drop.
Built feature pipelines.
Designed real-time feature pipeline using Kafka + Flink processing 500K events/minute with <2s feature freshness, supporting fraud detection model serving ₹1,000Cr+ daily transactions.
Beyond the basic skills list, these are the terms that differentiate senior candidates from mid-level ones in ATS scoring. If you have this experience, make sure it's visible on your resume.
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