MLOps Engineer

Vrinda International; India; On-site; INR 1,275,000 - 1,725,000; Full time

Job summary

Vrinda International is seeking an MLOps Engineer to bridge data science and production environments, focusing on automated ML deployment lifecycles. You will streamline model deployment, ensure scalability, and optimize performance, contributing to the organization’s AI initiatives.

The ideal candidate has experience in Python, Docker, Kubernetes, and end-to-end ML deployment, with a strong emphasis on automation and production reliability.

Qualifications

• 3–6 years of relevant experience.
• Strong Python, Docker and Kubernetes skills.
• Experience with ML deployment pipelines, CI/CD and cloud platforms.

Responsibilities

• Design and implement automated pipelines for ML model deployment and CI.
• Manage cloud infrastructure and MLOps tools to ensure reliability.
• Oversee model versioning and experiment tracking for reproducibility.
• Containerize applications with Docker and orchestrate with Kubernetes.
• Diagnose and resolve production issues to minimize downtime.
• Develop Python scripts to support ML workflows.
• Establish CI/CD practices tailored for ML models to accelerate delivery.

Skills

Python

CI/CD for ML

Automation

Cloud platforms

Experiment tracking

Model versioning

ML deployment

Tools

Docker

Kubernetes

Job description

Job Description:

MLOps Engineer

Summary

We are seeking a skilled MLOps Engineer to join our IT Services team, where you will play a pivotal role in bridging the gap between data science and production environments. This position is dedicated to streamlining the deployment lifecycle of machine learning models, ensuring they are robust, scalable, and efficient. The ideal candidate will leverage their expertise in automation and performance optimization to drive the successful integration of AI solutions into our business operations, directly contributing to the organizations technological advancement and operational excellence.

Responsibilities

• Design and implement automated pipelines for machine learning model deployment and continuous integration.
• Manage cloud infrastructure and utilize specialized MLOps tools to maintain system reliability.
• Oversee model versioning and experiment tracking to ensure reproducibility and auditability of all data science projects.
• Containerize applications using Docker and orchestrate workloads with Kubernetes for scalable deployment.
• Diagnose and resolve production issues related to machine learning systems to minimize downtime.
• Develop and maintain Python-based scripts and applications to support ML workflows.
• Establish and enforce CI/CD practices specifically tailored for machine learning models to accelerate delivery cycles.

Requirements

• Experience: 3-6 years of professional experience in relevant technical roles.
• Technical Proficiency: Strong command of Python programming and experience with Docker and Kubernetes.
• Domain Knowledge: Proven expertise in MLOps tools, cloud platforms, and CI/CD pipelines for machine learning.
• Core Skills: Demonstrated ability in automation, performance optimization, model versioning, experiment tracking, and production troubleshooting.
• Deployment: Hands‑on experience with the end‑to‑end deployment of ML models in production environments.
• Compensation: Salary range of ₹15 LPA.