Senior ML Engineer - Remote, Cloud ML Pipelines

Encora; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Corfoge (Coforge) is looking for a Senior Machine Learning Engineer to design, develop, and deploy scalable ML solutions in production. You will build end-to-end ML pipelines, implement MLOps, and manage models across AWS, Azure, and GCP.

You will collaborate with data scientists and product teams to translate requirements into robust ML applications and optimize workloads in Kubernetes environments. Remote work is supported globally.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent practical experience).
• 5+ years of experience as a Machine Learning Engineer, focused on production-grade ML systems.
• Strong programming experience with Python, SQL, and PySpark for large-scale data processing.
• Hands-on experience with ML frameworks and libraries such as Scikit-learn, PyTorch, TensorFlow, and XGBoost.
• Proven expertise building and maintaining robust ML pipelines using MLflow or similar MLOps platforms.
• Experienced deploying and managing ML solutions in cloud environments (AWS, Azure, GCP, Databricks).
• Strong understanding of the full ML lifecycle, from data preparation to production monitoring and maintenance.
• Experience working with Kubernetes and containerized workloads for ML applications.

Responsibilities

• Design, develop, and deploy scalable ML solutions in production environments.
• Build and optimize end-to-end ML pipelines (data ingestion, feature engineering, model training, evaluation, deployment, monitoring).
• Develop distributed data processing workflows using PySpark and SQL for large-scale ML applications.
• Collaborate with Data Scientists, Data Engineers, Product Teams, and stakeholders to translate requirements into ML solutions.
• Deploy and manage ML models across cloud platforms (AWS, Azure, GCP, Databricks).
• Implement MLOps best practices (model versioning, experiment tracking, CI/CD, monitoring, lifecycle management).
• Design and maintain containerized ML workloads leveraging Kubernetes for serving, batch processing, and orchestration.
• Drive improvements in model performance, scalability, reliability, and operational efficiency.

Skills

Python

SQL

PySpark

MachineLearning

Scikit-learn

PyTorch

XGBoost

TensorFlow

ML Pipelines

MLflow

Databricks

AWS

Azure

GCP

Kubernetes

MLOps

Education

Bachelor’s degree in Computer Science or related field

Master’s degree in Computer Science or related field

Tools

Docker

Kubernetes

Databricks

Job description

Corfoge (Coforge) is looking for a Senior Machine Learning Engineer to design, develop, and deploy scalable ML solutions in production. You will build end-to-end ML pipelines, implement MLOps, and manage models across AWS, Azure, and GCP.

You will collaborate with data scientists and product teams to translate requirements into robust ML applications and optimize workloads in Kubernetes environments. Remote work is supported globally.