Freelance MLOPS Engineer

IndiTech Consulting; India; Remote; INR 1,200,000 - 2,400,000; Full time

Job summary

IndiTech Consulting is seeking an MLOps Engineer to design, deploy, and manage scalable ML pipelines in production. The role emphasizes hands-on Python, ML frameworks, and cloud-based ML lifecycle automation in a remote-friendly environment.

You will collaborate with data scientists and engineers to productionize models, ensure reliability, and implement governance for ML systems. Strong CI/CD and cloud deployment experience are required.

Qualifications

• Proficient in Python with hands-on ML experience.
• Experience building, deploying, and maintaining end-to-end ML pipelines.
• Familiarity with cloud platforms and ML lifecycle automation.

Responsibilities

• Design end-to-end ML pipelines from data ingestion to deployment.
• Build and maintain CI/CD pipelines for ML model training, testing, and deployment.
• Automate model monitoring, retraining, and performance tracking.

Skills

Python

Data pipelines

Distributed systems

ML concepts

CI/CD concepts

Tools

MLflow

Kubeflow

Airflow

AWS SageMaker

Azure ML

Jenkins

GitHub Actions

GitLab CI

Job description

We are looking for a skilled MLOps Engineer to help design, deploy, automate, and manage scalable machine learning pipelines in production environments.

If you have hands-on experience in Python, ML frameworks, cloud platforms, and ML lifecycle automation, this is an excellent opportunity to contribute to enterprise-level machine learning solutions in a flexible, remote working environment.

Key Responsibilities:

• Design and implement end-to-end machine learning pipelines, from data ingestion to model deployment.
• Build and maintain CI/CD pipelines for ML model training, testing, validation, and deployment.
• Automate model monitoring, retraining, performance tracking, and optimization.
• Collaborate with Data Scientists and Data Engineers to productionize machine learning models.
• Ensure scalability, reliability, security, and reproducibility of ML systems.
• Manage model versioning, experiment tracking, and ML lifecycle management.
• Implement governance, compliance, and best practices for production ML environments.

Required Skills & Expertise:

• Strong programming skills in Python.
• Experience with TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, Airflow, AWS SageMaker, or Azure ML.
• Knowledge of CI/CD tools such as Jenkins, GitHub Actions, and GitLab CI.
• Experience with AWS cloud services and ML deployment infrastructure.
• Strong understanding of data pipelines, ETL processes, and distributed systems.
• Experience with model monitoring, automated retraining, and production ML operations.