Offer summary

Qualifications:

Experience monitoring machine learning models in production., Knowledge of tools like WhyLabs, Splunk, and Datadog., Familiarity with AKS and Databricks., Comfortable with Python or SQL for debugging and building workflows..

Key responsibilities:

Monitor machine learning models for issues like data/model drift.

Set up tools to track model performance.

Work on the full lifecycle of models, including deployment and retraining.

Collaborate with the team to ensure models remain accurate and useful.

World leader in engineering and R&D services, Capgemini Engineering combines its broad industry knowledge and cutting-edge technologies in digital and software to support the convergence of the physical and digital worlds. Coupled with the capabilities of the rest of the Group, it helps clients to accelerate their journey towards Intelligent Industry. Capgemini Engineering has 60,000 engineer and scientist team members in over 30 countries across sectors including Aeronautics, Space, Defense, Naval, Automotive, Rail, Infrastructure & Transportation, Energy, Utilities & Chemicals, Life Sciences, Communications, Semiconductor & Electronics, Industrial & Consumer, Software & Internet. Capgemini Engineering is an integral part of the Capgemini Group, a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, cloud and data, combined with its deep industry expertise and partner ecosystem. The Group reported 2023 global revenues of €22.5 billion. Get The Future You Want | www.capgemini.com

Job description

Descripción breve

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unluck the value of technology and build a more sustainable, more inclusive world.

Responsibilities

Monitor machine learning models for issues like data/model drift.
Set up tools like WhyLabs, Splunk, and Datadog to track model performance.
Work on the full lifecycle of models, including deployment, monitoring, and retraining.
Help improve how we manage and monitor data pipelines.
Collaborate with the team to make sure models stay accurate and useful.

Required Skills

Experience monitoring machine learning models in production.
Knowledge of tools like WhyLabs, Splunk, and Datadog.
Familiarity with AKS and Databricks.
Understanding of how data moves through systems and how to keep it reliable.
Comfortable with Python or SQL for debugging and building workflows.
Understanding of MLOps best practices.
Experience with cloud tools like Azure Cosmos DB.
Familiarity with integrating models into BI tools for reporting.

About Capgemini

At Capgemini Engineering, the world leader in engineering services, we bring together a global team of engineers, scientists, and architects to help the world’s most innovative companies unleash their potential. From autonomous cars to lifesaving robots, our digital and software technology experts think outside the box as they provide unique R&D and engineering services across all industries. Join us for a career full of opportunities. Where you can make a difference. Where no two days are the same.

Required profile