Machine Learning Engineer

Yantran; Chennai District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Yantran is seeking a Machine Learning Engineer to build scalable models and production systems in Chennai. You will develop and train models, perform feature engineering, and create robust training pipelines.

You will deploy models to production environments, monitor performance, and collaborate with Data Scientists and Data Engineers to iterate on solutions. Strong Python and ML framework experience required; experience with Docker and cloud platforms is a plus.

Qualifications

• Experience building and deploying ML models.
• Proficiency with Python and ML frameworks.
• Strong data preprocessing and feature engineering skills.
• Ability to develop scalable ML pipelines.

Responsibilities

• Develop and train machine learning models.
• Perform feature engineering and data preprocessing.
• Build model-training pipelines.
• Deploy ML models into production environments.
• Optimize model performance.
• Implement model monitoring and versioning.
• Collaborate with Data Scientists and Data Engineers.
• Develop ML APIs and services.
• Support continuous improvement of ML models.

Skills

Python

Scikit-learn

TensorFlow or PyTorch

Pandas/NumPy

SQL

Machine Learning algorithms

Model deployment

Tools

Docker

Cloud platforms

MLOps

Job description

We are looking for a Machine Learning Engineer to build scalable machine learning models and production systems.

Responsibilities:

• Develop and train machine learning models.
• Perform feature engineering and data preprocessing.
• Build model-training pipelines.
• Deploy ML models into production environments.
• Optimize model performance.
• Implement model monitoring and versioning.
• Collaborate with Data Scientists and Data Engineers.
• Develop ML APIs and services.
• Support continuous improvement of ML models.

Required Skills:

• Python.
• Scikit-learn.
• TensorFlow or PyTorch.
• Pandas/NumPy.
• SQL.
• Machine Learning algorithms.
• Model deployment.
• Docker.
• Cloud platforms.
• MLOps preferred.