Senior Machine Learning Engineer

G10X Pte Ltd; United States; Remote; USD 31,000 - 62,000; Full time

Job summary

We are seeking a Senior Machine Learning Engineer with strong hands-on MLOps experience to deploy and maintain production ML systems. The role emphasizes operationalizing models, monitoring performance, and managing lifecycle across multimodal data (vision, text, metadata).

You will build scalable PySpark and Databricks pipelines, leverage cloud platforms like AWS/Azure/GCP, and collaborate with cross-functional teams to deliver robust, production-ready ML solutions.

Qualifications

• 5+ years of hands-on experience in Machine Learning / ML Engineering.
• Strong hands-on MLOps and production ML deployment experience.
• Experience with PySpark, Databricks, and Python in production ML systems.
• Experience deploying Deep Learning models into production.
• Solid understanding of the end-to-end MLOps lifecycle.
• Experience with multimodal data (CV + text + metadata).
• Experience with at least one cloud platform (AWS/Azure/GCP).

Responsibilities

• Design, deploy, and operationalize Machine Learning and Deep Learning models in production.
• Build and maintain end-to-end MLOps workflows including training, validation, deployment, monitoring, versioning, and retraining.
• Develop scalable data and ML pipelines using PySpark and Databricks.
• Productionize Deep Learning models and support scalable model inference.
• Work with multimodal data (CV, text, metadata) to build ML solutions.
• Implement model monitoring, performance tracking, drift detection, experiment tracking, and lifecycle management.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and stakeholders to deliver production-ready ML solutions.
• Ensure ML systems are scalable, reliable, maintainable, and suitable for production environments.
• Work with cloud-based ML infrastructure and deployment platforms.

Skills

MLOps

Production ML deployment

End-to-end ML lifecycle

Cloud platforms (AWS/Azure/GCP)

Multimodal data (CV + text)

Model monitoring & drift detection

Python programming

Tools

PySpark

Databricks

Python

Job description

Senior Machine Learning Engineer

Location: India Remote
Experience: 5+ years
Employment Type: Full-time

Role Overview

We are looking for a Senior Machine Learning Engineer with strong hands-on experience in MLOps, production ML systems, PySpark, and Databricks.

The role is focused more on deploying, operationalizing, monitoring, and maintaining ML/DL models in production than on pure research or model development.

Key Responsibilities

• Design, deploy, and operationalize Machine Learning and Deep Learning models in production.
• Build and maintain end-to-end MLOps workflows, covering model training, validation, deployment, monitoring, versioning, and retraining.
• Develop scalable data and ML pipelines using PySpark and Databricks.
• Productionize Deep Learning models and support scalable model inference.
• Work with multimodal data, combining Computer Vision, textual data, metadata, and other signals to build ML solutions.
• Implement model monitoring, performance tracking, drift detection, experiment tracking, and lifecycle management.
• Work with cloud-based ML infrastructure and deployment platforms.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and business stakeholders to deliver production-ready ML solutions.
• Ensure ML systems are scalable, reliable, maintainable, and suitable for production environments.

Mandatory Skills

• 5+ years of experience in Machine Learning / ML Engineering.
• Strong hands-on MLOps and production ML deployment experience.
• PySpark - mandatory.
• Databricks - mandatory.
• Experience deploying Deep Learning models into production.
• Strong understanding of the end-to-end MLOps lifecycle.
• Experience working with Computer Vision + textual data + metadata or other multimodal data.
• Experience with at least one cloud platform such as AWS, Azure, or GCP.
• Strong Python programming skills.

Good to Have

• Strong understanding of Deep Learning architectures.
• Experience building Deep Learning models using PyTorch or TensorFlow.
• Experience with Generative AI / RAG.
• Understanding and practical experience with embeddings, vector databases, and semantic search.
• Experience with MLflow or similar ML lifecycle/experiment tracking tools.
• Experience with Kubernetes, CI/CD, model serving, or ML monitoring platforms.