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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.
Our team consists of 100+ engineers, designers, data scientists, implementation, and product people, working in small inter-disciplinary teams closely with creative agencies, media agencies, and with our customers, to develop and scale our leading digital advertising optimization suite that delivers amazing outcomes for brands and audiences.
Our platforms are built with Python, React, and Clojure, are deployed using CI/CD, heavily exploit automation, and run on AWS, GCP, k8s, Snowflake, BigQuery, and more. We serve 9 petabytes and 77 billion objects annually, optimize thousands of campaigns to maximise ROI, and deliver 20 billion ad impressions across the globe. You’ll play a leading role in significantly scaling this further.
As our first Machine Learning Operations (MLOps) Engineer, you will play a pivotal role in bridging the gap between platform engineering, data science, and software engineering, building systems that drive the deployment, monitoring, and scalability of machine learning models. You will design and implement pipelines, automate workflows, and optimise model performance in training and production environments. You’ll lead the creation of process, implementation of tools, and creation of solutions mature how we integrate machine learning solutions into our production systems, while maintaining reliability, security, and efficiency. You’ll additionally play a leading role in driving continuous improvement in model lifecycle management, from development to deployment and monitoring.
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 360,000 team members globally in more than 50 countries. With its strong 55-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.
Soft Skills:
Experience:
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.
Our team consists of 100+ engineers, designers, data scientists, implementation, and product people, working in small inter-disciplinary teams closely with creative agencies, media agencies, and with our customers, to develop and scale our leading digital advertising optimization suite that delivers amazing outcomes for brands and audiences.
Our platforms are built with Python, React, and Clojure, are deployed using CI/CD, heavily exploit automation, and run on AWS, GCP, k8s, Snowflake, BigQuery, and more. We serve 9 petabytes and 77 billion objects annually, optimize thousands of campaigns to maximise ROI, and deliver 20 billion ad impressions across the globe. You’ll play a leading role in significantly scaling this further.
As our first Machine Learning Operations (MLOps) Engineer, you will play a pivotal role in bridging the gap between platform engineering, data science, and software engineering, building systems that drive the deployment, monitoring, and scalability of machine learning models. You will design and implement pipelines, automate workflows, and optimise model performance in training and production environments. You’ll lead the creation of process, implementation of tools, and creation of solutions mature how we integrate machine learning solutions into our production systems, while maintaining reliability, security, and efficiency. You’ll additionally play a leading role in driving continuous improvement in model lifecycle management, from development to deployment and monitoring.
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 360,000 team members globally in more than 50 countries. With its strong 55-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.
,[ 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 Infrastructure and Pipeline Management: , Design and implement scalable MLOps pipelines for data ingestion, transformation, and model deployment. , Build infrastructure-as-code solutions using tools like Terraform to manage cloud environments (AWS, GCP) Collaboration with Teams: , Work closely with data scientists to operationalize machine learning models. , Collaborate with software engineers to integrate ML systems into broader platforms Cloud and Big Data Expertise: , Utilize cloud services from AWS, GCP, and Snowflake for scalable data storage and processing. DevOps Integration: , Implement CI/CD pipelines and automations to streamline ML model deployment. , Use containerization 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 Requirements: Python, GCP, Data warehouses, Snowflake, BigQuery, MLOps, Kubeflow, MLflow, DevOps, Jenkins, GitLab, f.lux, Docker, Kubernetes, TensorFlow, SQL, NoSQL, Distributed computing, Amazon EMR, Spark, Hadoop, Communication skills, Clojure, Cloud platform Additionally: Training budget, Sport subscription, Private healthcare, International projects, Free coffee, Bike parking, Free parking, Mobile phone, In-house trainings, Modern office, No dress code.