Offer summary

Qualifications:

5 years of experience as a Machine Learning Engineer or in similar roles., Strong experience with Unity Catalog in Databricks for managing data assets., Proficiency in Python and Spark for data engineering tasks., Advanced English proficiency..

Key responsabilities:

Set up and manage Unity Catalog in Databricks for data access organization.

Design and operationalize Feature Stores for machine learning models.

Build efficient data pipelines for processing features in ML workflows.

Collaborate with teams using Azure tools to integrate data solutions.

Job description

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 life-saving 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.

YOUR ROLE

Set up and manage Unity Catalog in Databricks to organize and secure data access across teams.
Design and operationalize Feature Stores to support machine learning models in production.
Build efficient data pipelines to process and serve features to ML workflows.
Collaborate with teams using Databricks, Azure Cosmos DB, and other Azure tools to integrate data solutions.
Monitor and optimize the performance of pipelines and feature stores.

YOUR PROFILE

5 years of experience as a ML Engineer or similar roles.
Strong experience with Unity Catalog in Databricks for managing data assets and access control.
Hands-on experience working with Databricks Feature Store or similar solutions.
Knowledge of building and maintaining scalable ETL pipelines in Databricks
Familiarity with Azure tools like Azure Cosmos DB and ACR
Understanding of machine learning workflows and how feature stores fit into the pipeline.
Strong problem-solving skills and a collaborative mindset.
Proficiency using Java (specifically Java APIM) to deploy Machine Learning Models.
Proficiency in Python and Spark for data engineering tasks.
Experience with monitoring tools like Splunk or Datadog to ensure system reliability.
Familiarity with AKS for deploying and managing containers.
Advanced English.

WHAT YOU’LL LOVE ABOUT WORKING HERE?

At Capgemini Engineering, we encourage flexibility in how, when, and where people get their work done, allowing a better work-life balance, and greater empowerment. They partner with their managers to find an arrangement that works best for their role and their circumstances.
At Capgemini Engineering, we’re always looking ahead. We’re part of a team that creates opportunities to achieve valuable change. Change that makes a difference. New connections, new technologies, new ways to work. It’s so energizing.
At Capgemini Engineering, we make it easy for you to deepen knowledge and learn new skills while you’re still doing the day job.

About Capgemini

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of over 300,000 team members in nearly 50 countries. With its strong 50-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering, and platforms.

Get the future you want!

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