Senior Machine Learning Engineer

G10X Pte Ltd; India; Remote; INR 3,500,000 - 7,000,000; Full time

Benefits offered by this job

Job summary

G10X Pte Ltd is hiring a Senior Machine Learning Engineer to deploy and maintain production ML systems. You will own end-to-end MLOps workflows using PySpark and Databricks, focusing on deployment, monitoring, and lifecycle management.

You will collaborate with data scientists and engineers to build scalable ML pipelines, handle multimodal data including CV and text, and ensure reliability across cloud platforms such as AWS/Azure/GCP. 5+ years required.

Qualifications

• 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.

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.

Skills

ML Engineering

MLOps

PySpark

Databricks

Deep Learning

Python

Cloud Platforms

ML Lifecycle

Multimodal Data

Model Monitoring

Kubernetes

CI/CD

MLflow

Embeddings

Vector Databases

Generative AI

RAG

TensorFlow

PyTorch

Tools

MLflow

Kubernetes

CI/CD pipelines

Vector DBs

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.