Overview

Capgemini Engineering is a world leader in engineering services. Our team brings together engineers, scientists, and architects to help innovative companies unlock their potential. From autonomous cars to life‑saving robots, our digital and software technology experts think outside the box, delivering unique R&D and engineering services across all industries.

YOUR ROLE

Our team of 100+ engineers, designers, data scientists and implementation and product people work in small interdisciplinary teams closely with creative agencies, media agencies, and customers. We develop and scale our leading digital advertising optimization suite that delivers amazing outcomes for brands and audiences. Platforms are built with Python, React, Clojure, deployed using CI/CD, explore automation, run on AWS, GCP, k8s, Snowflake, BigQuery. We serve 9 petabytes and 77 billion objects annually, optimise thousands of campaigns to maximise ROI, and deliver 20 billion ad impressions worldwide. As the first Machine Learning Operations (MLOps) Engineer, you will bridge the gap between platform engineering, data science and software engineering, building systems that drive deployment, monitoring and scalability of machine learning models.

You design and implement pipelines, automate workflows and optimise model performance in training and production environments. You lead the creation of processes, implementation of tools and mature solutions how we integrate machine learning solutions into our production systems while maintaining reliability, security and efficiency. You additionally play a leading role in driving continuous improvement in model lifecycle management, from development to deployment and monitoring.

RESPONSIBILITIES

• Deploy, monitor and maintain machine learning models in production environments.
• Automate model training, retraining, versioning and governance processes.
• Monitor model performance, detect drift and ensure scalability and reliability of ML workflows.
• Design and implement scalable MLOps pipelines for data ingestion, transformation and model deployment.
• Build infrastructure‑as‑code solutions using Terraform to manage cloud environments (AWS, GCP).
• Work closely with data scientists to operationalise machine learning models.
• Collaborate with software engineers to integrate ML systems into broader platforms.
• Utilise cloud services from AWS, GCP and Snowflake for scalable data storage and processing.
• Implement CI/CD pipelines and automations to streamline ML model deployment.
• Use containerisation tools like Docker and orchestration platforms like Kubernetes for scalable deployments.
• Use observability platforms to monitor pipeline and operational health of model production, delivery and execution.

YOUR PROFILE

Technical Skills

• Proficiency in Python for ML development; familiarity with additional languages like Clojure is a plus.
• Expertise in cloud platforms (AWS, GCP) and data warehouses like Snowflake or BigQuery.
• Strong knowledge of MLOps frameworks (e.g., Kubeflow, MLflow) and DevOps tools (e.g., Jenkins, GitLab, Flux).
• Experience with containerisation (Docker) and orchestration (Kubernetes).
• Experience with infrastructure‑as‑code tools like Terraform.

Machine Learning Knowledge

• Solid understanding of machine learning principles, including model evaluation, explainability and retraining workflows.
• Hands‑on experience with ML frameworks such as TensorFlow or PyTorch.

Big Data Handling

• Proficiency in SQL/NoSQL databases and distributed computing systems like Dataprov, EMR, Spark, Hadoop.

Soft Skills

• Strong communication skills to collaborate across multidisciplinary teams.
• Problem‑solving mindset with the ability to work in agile environments.

Experience

• At least 5+ years in platform, software or MLOps engineering roles.
• Proven track record of deploying scalable ML solutions in production environments.

What You’ll Love About Working Here

• Well‑being culture: medical care with Medicover, private life insurance, Sports card, and a Capgemini Helpline offering therapeutic support. Educational podcast Let’s talk about wellbeing available on Spotify.
• Access to over 70 training tracks with certification opportunities on our NEXT platform. Free access to Education First, Pluralsight, TED Talks, Coursera and Udemy Business material and trainings.
• Continuous feedback and ongoing performance discussions thanks to GetSuccess and a transparent performance management policy.
• Hybrid working model: after onboarding connect work from a modern office with ergonomic work from home, thanks to a home office package (laptop, monitor, chair). Ask recruiter for details.

Diversity and Inclusion

Capgemini is committed to diversity and inclusion, ensuring fairness in all employment practices. We evaluate individuals based on qualifications and performance, not personal characteristics, striving to create a workplace where everyone can succeed and feel valued.

Apply now!