Resume Example & Template

Machine Learning Engineer Resume Example

What the role is, a real ML Engineer resume example, and exactly what recruiters look for — then build your own in minutes.

Build your Machine Learning Engineer resume

What is a Machine Learning Engineer?

A Machine Learning Engineer designs, builds, and ships models into production. They sit between data science and software engineering — turning experiments and notebooks into reliable, scalable services that make predictions on real traffic, and keeping those models accurate as data drifts over time.

Day to day the role spans feature engineering, model training and evaluation, and the MLOps that surrounds it: pipelines, experiment tracking, model registries, deployment, monitoring, and retraining. Strong ML Engineers care as much about latency, cost, and reliability as they do about model accuracy.

As companies move machine learning and LLMs from prototype to product, the ML Engineer has become one of the most in-demand technical roles of 2026 — valued for the rare combination of solid software engineering and applied ML depth.

Key skills for a Machine Learning Engineer resume

  • Python and ML frameworks (PyTorch, TensorFlow, scikit-learn)
  • Feature engineering & model evaluation
  • MLOps — pipelines, experiment tracking (MLflow, W&B), model registry
  • Model serving & deployment (Docker, Kubernetes, FastAPI, Triton)
  • Cloud ML platforms (SageMaker, Vertex AI, Azure ML)
  • Data pipelines & SQL (Spark, Airflow, dbt)
  • Monitoring, drift detection & retraining
  • LLMs, embeddings & vector databases

Machine Learning Engineer resume example

Priya Nair

Machine Learning Engineer

Berlin, Germany

Summary

Machine Learning Engineer with 6+ years shipping models to production for recommendation, ranking, and NLP. Strong across the full lifecycle — feature engineering, training, deployment, and monitoring — with a focus on latency, cost, and reliability. Recently led LLM and retrieval features into production.

Experience

Machine Learning Engineer · Zalando

Apr 2022 – Present

  • Built and deployed a real-time ranking model serving 20M+ requests/day, lifting click-through rate 12% and revenue per session 8%.
  • Owned the MLOps stack end-to-end — MLflow tracking, containerised serving on Kubernetes, and automated drift detection with weekly retraining.
  • Shipped an LLM-powered search feature using embeddings and a vector database, cutting “no result” queries by 35%.
  • Reduced inference latency 40% and serving cost 30% via model distillation and batching.

Data Scientist → ML Engineer · Delivery Hero

Jul 2019 – Mar 2022

  • Developed demand-forecasting models (gradient boosting, LSTM) that improved courier allocation efficiency 18%.
  • Migrated notebook prototypes to production pipelines on AWS SageMaker with CI/CD and monitoring.

Education

MSc Data Engineering & Analytics

Technical University of Munich (TUM) · 2017 – 2019

BE Computer Science

BITS Pilani · 2013 – 2017

Certifications

  • AWS Certified Machine Learning — Specialty
  • DeepLearning.AI TensorFlow Developer

Skills

ML: PyTorch · TensorFlow · scikit-learn · XGBoost · Hugging Face

MLOps: MLflow · Docker · Kubernetes · Airflow · CI/CD

Cloud & Data: AWS SageMaker · Spark · SQL · Feature Store

LLM & Serving: Embeddings · Vector DB · FastAPI · Triton

Use this as your starting point

How to write a Machine Learning Engineer resume that stands out

  • Show models in PRODUCTION, not just notebooks — deployment, traffic served, and business impact are what separate an ML Engineer from a data scientist.
  • Quantify model and system impact (e.g. “improved conversion 12%”, “cut inference latency 40%”, “reduced serving cost 30%”).
  • Name the exact stack — framework, serving layer, cloud ML platform, and MLOps tooling. Technical screeners and ATS look for specific tools.
  • Highlight MLOps maturity: CI/CD for models, monitoring, drift detection, and automated retraining. It signals you can own a model end-to-end.
  • Include one line on data scale and latency requirements — it tells reviewers the seniority of the systems you have run.

Machine Learning Engineer resume — FAQ

What does a Machine Learning Engineer do?

A Machine Learning Engineer builds, deploys, and maintains machine learning models in production. They handle feature engineering, training and evaluation, and the MLOps around it — pipelines, serving, monitoring, and retraining — so models run reliably and stay accurate on real data.

What skills should a Machine Learning Engineer put on a resume?

Python and ML frameworks (PyTorch, TensorFlow, scikit-learn), feature engineering and evaluation, MLOps tooling (MLflow, Docker, Kubernetes, CI/CD), a cloud ML platform (SageMaker, Vertex AI, Azure ML), data/SQL pipelines, and monitoring/drift handling. Familiarity with LLMs, embeddings, and vector databases is increasingly expected.

How is a Machine Learning Engineer different from a data scientist?

A data scientist focuses on analysis, experimentation, and modelling to answer questions. A Machine Learning Engineer focuses on productionising models — engineering reliable, scalable services, deploying them, and keeping them healthy in production. ML Engineers lean more toward software engineering and MLOps.

What should a Machine Learning Engineer resume include?

Lead with a summary that frames you as an engineer who ships models to production. Show deployed models with quantified impact, the exact ML and MLOps stack, data scale and latency, and end-to-end ownership (training → serving → monitoring). Keep it ATS-safe with clean structure and real text.

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