MLOps Engineer

SourcingXPress; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

SourcingXPress is seeking an MLOps Engineer to deploy, monitor, and maintain ML models in production across Azure, AWS, and Databricks. You will collaborate with data scientists and engineers to integrate models into core business systems and pipelines.

The role requires hands-on cloud-based infrastructure, Databricks, and MLOps best practices, with emphasis on automation, observability, and scalable deployments.

Qualifications

• 3–5 years of experience in MLOps, DevOps, or related fields.

Responsibilities

• Deploy machine learning models into production environments on Azure, AWS, and Databricks.
• Collaborate with data scientists and engineering teams to integrate ML models into existing business systems and pipelines.
• Set up and manage infrastructure for scalable ML model training and deployment using cloud platforms and Databricks.
• Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, and Databricks Workflows.
• Monitor model performance, data drift, concept drift, and system health using observability tools.
• Implement alerting systems and dashboards for real‑time monitoring and quick issue resolution.
• Hands‑on experience with Azure Machine Learning Services, AWS SageMaker, and Databricks.
• Use Databricks MLflow, Delta Lake, and other tools for model tracking, experiment management, and data versioning.
• Automate model deployment and workflow orchestration using Python, Databricks Jobs, and Airflow.
• Implement Infrastructure as Code (IaC) using Terraform, ARM templates, or CloudFormation for scalable deployments.

Skills

Python

Shell scripting

CI/CD

Machine learning

Problem solving

Tools

Databricks

Azure

AWS

Kubernetes

Docker

Apache Airflow

MLflow

Delta Lake

Terraform

GitHub Actions

Azure DevOps

Jenkins

Job description

MLOps Engineer – Job Description

We are looking for a skilled and experienced MLOps Engineer to join our team and play a key role in deploying, maintaining, and monitoring machine learning models in production environments.

This role requires a solid understanding of cloud-based infrastructure, Databricks, automation, and MLOps best practices. You will collaborate closely with data scientists, engineers, and DevOps teams to ensure scalable, secure, and efficient machine learning operations.

Key Responsibilities

• Deploy machine learning models into production environments on Azure, AWS, and Databricks.
• Collaborate with data scientists and engineering teams to integrate ML models into existing business systems and pipelines.
• Set up and manage infrastructure for scalable ML model training and deployment using cloud platforms and Databricks.
• Implement CI/CD pipelines for ML workflows using tools like Azure DevOps, GitHub Actions, and Databricks Workflows.
• Monitor model performance, data drift, concept drift, and system health using appropriate observability tools.
• Implement alerting systems and dashboards for real‑time monitoring and quick issue resolution.
• Hands‑on experience with Azure Machine Learning Services, AWS SageMaker, and Databricks.
• Use Databricks MLflow, Delta Lake, and other tools for model tracking, experiment management, and data versioning.
• Automate model deployment and workflow orchestration using Python, Databricks Jobs, and Airflow.
• Implement Infrastructure as Code (IaC) using Terraform, ARM templates, or CloudFormation for scalable deployments.

Required Skills & Qualifications

• 3–5 years of experience in MLOps, DevOps, or related fields with direct experience in ML model deployment and monitoring.
• Proficient in Databricks, Azure, and AWS environments.
• Hands‑on experience with Kubernetes, Docker, Apache Airflow, MLflow, Databricks, and Delta Lake.
• Experience building robust CI/CD pipelines for ML using Git, Azure DevOps, Jenkins, or GitHub Actions.
• Strong programming/scripting skills in Python; familiarity with shell scripting is a plus.
• Experience provisioning infrastructure using Terraform or similar tools.
• Understanding of model versioning, governance, reproducibility, and compliance in enterprise environments.
• Problem‑solving ability to identify bottlenecks, optimize workflows, and deliver scalable solutions across the ML lifecycle.

Equal Employment Opportunity Statement

C5i is an equal‑opportunity employer. We are committed to equal employment opportunity regardless of race, color, religion, sex, sexual orientation, age, marital status, disability, gender identity, and other protected characteristics. If you have a disability or special need that requires accommodation, please let us know during the hiring stages.