Machine Learning Engineer CV — example, template & tips for India 2026
Build an ATS-optimised machine learning engineer CV highlighting production ML systems, model performance, and MLOps experience. For ML engineering roles at AI companies, research labs, and top Indian product startups.
Salary range (India)
₹8L – ₹60L+ depending on production experience and company tier
Top hiring companies
Google DeepMind · Amazon AI · Microsoft Research · Flipkart AI · Swiggy
What to include in a Machine Learning Engineer CV
- Model performance metrics: accuracy, F1 score, precision/recall, latency improvements after optimisation
- Production ML: model serving infrastructure, A/B testing framework, feature stores, monitoring
- Framework depth: PyTorch or TensorFlow — specify which is your primary and show research-to-production path
- MLOps stack: MLflow, Kubeflow, Weights & Biases, DVC, SageMaker — show pipeline automation
- Data at scale: training data volume, GPU compute used, distributed training experience
- Research publications or Kaggle competition placements — strong differentiators for research-adjacent roles
Common Machine Learning Engineer CV mistakes
- No production context — "trained ML models" is weak; "deployed real-time inference system serving 5M predictions/day" is strong
- Confusing ML engineer and data scientist scope — MLE focuses on building systems, not just running experiments
- Missing MLOps stack — in 2026, ML engineers without deployment and monitoring experience are underprepared for most JDs
- Listing academic projects only — production ML experience on real data with real stakes is essential
- Generic model types without business context — "image classification model" vs "real-time defect detection system reducing production rejects by 23%"
Top skills for a Machine Learning Engineer CV
These keywords are heavily screened in ATS for machine learning engineer roles. Make sure they appear naturally in your CV.
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Check my keyword score freeMachine Learning Engineer CV — frequently asked questions
What is the difference between a machine learning engineer and a data scientist in India?
A machine learning engineer builds and maintains the systems that run ML in production: model serving infrastructure, feature pipelines, A/B testing frameworks, and monitoring systems. A data scientist builds and experiments with models to generate business insights, often working in notebooks and handing off to ML engineers for deployment. In India's current job market, the boundary is blurring — many "data scientist" JDs now expect production deployment capability, and many "ML engineer" JDs expect model-building depth. The cleanest signal: if your primary output is a running system, you are an MLE; if your primary output is an insight or model specification, you are a data scientist.
How important is a Kaggle rank or research publication for an ML engineer CV in India?
Kaggle ranks matter significantly for entry-level and junior ML roles — a Kaggle Expert or Master badge is a credible substitute for professional experience and is explicitly screened by many Indian startups and research teams. A top-percentile finish in a Kaggle competition relevant to the domain is a strong positive signal. Research publications matter most for roles at research labs (Google, Microsoft, Amazon Research) or AI-first companies (Sarvam, Krutrim) and IIT/IISC-affiliated positions. For production-focused MLE roles at startups, GitHub repositories with real ML projects and quantified results generally carry more weight than publications.
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