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About the Role

We are seeking a highly skilled Google Cloud (GCP) ML Optimization Consultant to lead outcome-based optimization assessments and deliver implementation-ready recommendations for fraud detection machine learning platforms on Google Cloud Platform (GCP). This role focuses on reducing infrastructure and model run costs while improving performance, scalability, reliability, and operational efficiency across GCP environments and ML pipelines.

Responsibilities

Perform hands-on assessment, design, and diagnostics of GCP environments to identify optimization opportunities.
Optimize compute, storage, and networking configurations to reduce operational costs while maintaining performance and reliability.
Implement monitoring, observability, and cost-management practices across GCP services.
Provide configuration-level and architecture-level recommendations with clear implementation guidance.
Support the development, validation, deployment, and optimization of fraud detection machine learning models.
Analyze model training and inference workloads to improve efficiency, scalability, and cost-effectiveness.
Deliver measurable improvements in compute utilization, inference speed, and automation.
Collaborate with data scientists and fraud analytics teams to enhance model performance and operational readiness.
Optimize end-to-end machine learning pipelines, including data ingestion, transformation, feature engineering, training, deployment, and inference.
Improve pipeline resilience, performance, scalability, and cost efficiency.
Identify bottlenecks and implement solutions to enhance throughput and reliability.

Requirements

GCP engineering: Hands-on design and diagnostics, optimize compute, storage, network trade-offs to reduce run cost, implement monitoring and observability for cost, performance, and reliability.
Fraud ML modelling: Support building, validating, and deploying fraud ML models end-to-end with demonstrated cost savings (e.g., reduced compute, faster inference, improved automation).
ML data pipelines: Optimize pipelines from ingest, transform, feature creations, training, inference, focusing on cost efficiency, performance, and resilience.
Big data distributed processing: Develop and tune large-scale batch/stream workloads (Spark or similar), improve job efficiency, stability, and operational readiness in production.
Software engineering: Strong Python, SQL, apply CI/CD, automated testing, modular design, and disciplined code reviews.

Work Type

Hybrid working

Benefits

Accredited Great Place to work for Wellbeing in 2024
Trained ‘Mental Health Champions’ across each business area
Investment in wellbeing apps such as Thrive and Peppy
Empowered to explore, innovate, and progress
Capgemini’s ‘learning for life’ mindset
Countless training and development opportunities from thinktanks to hackathons
Access to 250,000 courses with numerous external certifications from AWS, Microsoft, Harvard ManageMentor, Cybersecurity qualifications and much more
Joining one of the World’s Most Ethical Companies®

About the Company

Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value.
We imagine the future of organisations and make it real with AI, technology and people.
With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries.
We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations.
The Group reported 2024 global revenues of €22.1 billion.
Make it real | www.capgemini.com

Equal Opportunity

Capgemini is proud to be a Disability Confident Employer (Level 2) under the UK Government’s Disability Confident scheme.
As part of our commitment to inclusive recruitment, we will offer an interview to all candidates who: Declare they have a disability, and Meet the minimum essential criteria for the role.
Please opt in during the application process.