MLOps engineer

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MLops-virtual interview with Capgemini--all locations, Monday 27th Oct 2025 Fill out form

Position:

Harvest Level: Software Engineer

Machine Learning Ops Engineer is the one whos responsible for managing and optimizing the deployment and operation of machine learning models in production environments. He/she works closely with data scientists, software engineers, and DevOps teams to ensure that machine learning models are integrated seamlessly into existing systems and can scale effectively.

Role & Responsibilities

Responsible for design and implementation of secure and scalable infrastructure in Azure cloud.

Build and maintain CI/CD/CT pipeline across azure cloud platform for Data science projects.

Own and automate the infrastructure provisioning, demand forecasting and capacity planning.

Build tools and automation to improve systems availability, reliability, performance, monitoring and scalability.

Setting up the alerts and monitoring for the platform.

Monitoring system, application health, security controls and cost

Envision, implement and rollout best MLOPs/DevOps tools and automation.

Requirements:

Strong understanding of concepts related to Machine Learning, Architecture, and MLOps practices.

Proficient in Azure Cloud practices and managing Azure Kubernetes Service infrastructure.

Hands on experience in Python or any other scripting languages (Groovy, Shell)

Experience with monitoring tools.

Hands-on experience in Docker, Kubernetes.

Excellent experience with source code management tools (git)

Experience with modern cloud development practices such as Microservices Architecture, REST Interfaces etc.

Experience in implementing Data/Model drift.

Strive for Continuous Improvement and build continuous integration, continuous development, and constant deployment pipeline.

Excellent Troubleshooting skills

Always ready to learn more and adopt new cutting-edge technology with right value proposition.