Machine Learning Ops Engineer

Company Description

Quantum4U is a leading mobile app development company committed to enhancing global connectivity through innovative and scalable mobile applications. With a presence in the hands of over 70 million users worldwide, Quantum4U delivers top-tier utility apps that are secure, sustainable, and user-focused. The company offers comprehensive mobile app development solutions to businesses looking to establish a strong mobile presence and grow efficiently. Backed by a global team, Quantum4U ensures the highest quality standards in the mobile app industry.

Location: Gurgaon, Haryana

Experience: 6m -3 yrs

Role Overview

We are looking for a full-time Junior Machine Learning Operations Engineer to support the deployment, monitoring, and reliability of AI/ML systems in production. This role is ideal for candidates who enjoy working at the intersection of machine learning, infrastructure, and DevOps. You will help ensure that AI models move smoothly from development to production with scalable and reliable infrastructure.

Key Responsibilities

• Deploy AI/ML models into production environments
• Build and maintain Dockerized AI services
• Assist in setting up and managing CI/CD pipelines
• Configure and manage CPU/GPU environments for model execution
• Monitor model performance, system reliability, and uptime
• Support debugging, logging, and optimization of deployed models

Required Qualifications

• B.Tech / B.E. in Computer Science, IT, ECE, or a related field
• Strong quantitative and logical reasoning skills
• Working knowledge of Python and scripting
• Solid understanding of Linux fundamentals
• Hands-on experience with Docker

Must Have Skills

• Strong Linux fundamentals
• Hands-on Docker (image build, container run, docker-compose)
• Basic CI/CD understanding
• Python scripting
• Model deployment understanding (how to expose model via API)
• CPU/GPU environment basics
• Git usage

Good to Have

• Exposure to Kubernetes
• Experience with MLflow or model tracking tools
• Familiarity with AWS / GCP / Azure
• Understanding of LLM deployment workflows

Who Should Apply

• Candidates who enjoy hands-on infrastructure work
• Learners who build and deploy projects, not just experiments
• Engineers eager to grow in the MLOps domain

Experience Criteria

• 624 months of experience in DevOps / MLOps
• OR
• Strong academic or personal projects related to model deployment or infrastructure

Candidates with limited industry experience must provide GitHub projects

Why Join Us?

• Competitive salary
• Professional growth opportunities
• Fun, innovative work environment