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For Employers

MLOps Engineer

Weekday AI (YC W21)

8 years

20 - 40 Lacs

mumbai metropolitan region

Posted:13 hours ago| Platform:

Apply

Skills Required

learning ml data engineering design model training code terraform monitoring logging tracking devops automate scalability documentation deployment aws azure collaborative

Work Mode

On-site

Job Type

Full Time

Job Description

This role is for one of the Weekday's clients

Salary range: Rs 2000000 - Rs 4000000 (ie INR 20-40 LPA)

Min Experience: 8 yearsJobType: full-timeWe are looking for an experienced

MLOps Engineer

with a minimum of 8 years of experience to manage and scale machine learning operations across production environments. The role involves building robust, automated, and scalable ML pipelines, collaborating with data science and engineering teams, and deploying models efficiently using modern cloud and infrastructure tools. This position emphasizes hands-on experience with

MLOps frameworks

and

Terraform

, ensuring smooth end-to-end ML workflows.

Requirements

Key Responsibilities

Design, deploy, and manage end-to-end machine learning pipelines, from model training to production deployment
Implement infrastructure as code using Terraform for cloud-based ML environments
Ensure monitoring, logging, and performance tracking of ML models in production
Collaborate with data scientists, ML engineers, and DevOps teams to standardize and automate workflows
Build and maintain CI/CD pipelines for ML projects
Optimize resource utilization, ensure scalability, and troubleshoot issues in ML systems
Maintain documentation of ML operations, deployment procedures, and infrastructure setup

What Makes You a Great Fit

8+ years of hands-on experience in MLOps, cloud infrastructure, or production ML systems
Expertise in Terraform, MLOps frameworks, and cloud platforms (AWS, Azure, GCP)
Strong problem-solving skills and ability to optimize complex ML workflows
Collaborative mindset to work effectively with cross-functional teams
Proactive and adaptable, capable of handling multiple priorities in a fast-paced environment
Strong ownership and initiative to improve ML lifecycle efficiency

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MLOps Engineer