ML Ops Engineer

Miracle Software Systems, Inc.; India; Remote; INR 1,200,000 - 2,000,000; Full time

Job summary

A leading technology company in India is seeking an MLOps Engineer to design and maintain end-to-end pipelines for model training and deployment. The role involves developing backend services with Python and Java, implementing CI/CD workflows, and managing the model lifecycle on Google Cloud Platform. Candidates should have experience with Docker, Terraform, and Agile methodologies. This position offers a dynamic work environment focused on operationalizing machine learning models.

Responsibilities

• Design, build, and maintain MLOps pipelines.
• Develop backend services using Python and Java.
• Implement CI/CD workflows for machine learning.
• Manage full model lifecycle.
• Deploy workloads on Google Cloud Platform.
• Use Docker for application deployment.
• Implement Infrastructure as Code with Terraform.
• Establish model observability practices.
• Collaborate in Agile environments.
• Maintain SDLC best practices.

Skills

MLOps pipeline design

Python

Java

CI/CD workflows

Google Cloud Platform

Docker

Kubernetes

Terraform

Agile methodologies

Tools

BigQuery

GCS

Dataflow

Dataproc

GitHub

Tekton

SonarQube

42Crunch

FOSSA

Job description

Applicants are considered for employment without attention to or discrimination against race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. All employment is decided on the basis of qualifications, merit and business need.

Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, and continuous improvement in production environments.
• Develop backend services and APIs using Python and Java frameworks to operationalize machine learning models.
• Implement automated CI/CD workflows for machine learning and data applications.
• Manage the full model lifecycle, including feature engineering integration, model registry management, version control, and performance tracking.
• Deploy and operate machine learning workloads on Google Cloud Platform using BigQuery, GCS, Dataflow, and Dataproc.
• Deploy applications packaged using Docker and orchestrate deployments with Kubernetes.
• Implement Infrastructure as Code using Terraform for reproducible environment provisioning.
• Establish model observability practices, including drift detection, performance monitoring, and operational reliability controls.
• Collaborate with data scientists, platform engineers, and product teams within Agile delivery environments.
• Maintain SDLC best practices, including source control, security validation, static analysis, and automated quality checks using GitHub, Tekton, SonarQube, 42Crunch, and FOSSA.