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ML Engineer

Build and Deploy ML Models at Scale

Salary Expectations (₹ INR, per annum)

Fresher

₹7 – ₹12 LPA

Mid-Level

₹15 – ₹25 LPA

Senior

₹28 – ₹60+ LPA

8–10 Weeks

Build a strong base in Python programming and the fundamentals of machine learning.

Advanced Python — Object-Oriented Programming, Data Structures, and design patterns.

ML Theory — Math and implementation of core ML and Deep Learning algorithms.

ML Libraries — Scikit-Learn, TensorFlow/PyTorch, and Pandas.

Krish Naik YouTube Channel

End-to-end ML projects from model building to deployment.

Full Stack Deep Learning Free Course

The best course for production-level deep learning.

10–12 Weeks

The core of ML Engineering: automate the deployment, monitoring, and management of ML models.

CI/CD for ML — GitHub Actions to automate testing, training, and deployment of models.

Containerization — Package ML models and their dependencies into Docker containers.

Model Serving — Deploy models as scalable APIs using FastAPI, Flask, or Triton.

Monitoring — Track model performance, data drift, and concept drift in production.

MLOps Zoomcamp Free Course

A practical, hands-on MLOps course by DataTalks.Club.

AWS SageMaker / GCP Vertex AI Docs Documentation

Learn the tools provided by major cloud platforms.

4–6 Weeks

Learn to work with datasets that are too large to fit on a single machine.

Distributed Computing — Principles of distributed systems.

Apache Spark — Large-scale data processing and distributed model training.

Data Pipelines — Build and orchestrate data pipelines using Apache Airflow.

freeCodeCamp: Spark Course YouTube

An introduction to Apache Spark for big data processing.