Google Cloud GCP ML Optimization Consultant – London, UK

Google Cloud GCP ML Optimization Consultant – London, UK Overview

We are looking for a highly skilled Google Cloud (GCP) ML Optimization Consultant to lead optimization assessments and provide actionable recommendations for fraud detection machine learning platforms on Google Cloud Platform (GCP). This position emphasizes reducing infrastructure and model run costs while enhancing performance, scalability, reliability, and operational efficiency across GCP environments and machine learning pipelines.

Your Role:

• GCP Infrastructure Optimization: Conduct hands-on assessments, designs, and diagnostics of GCP environments to identify optimization opportunities. Optimize compute, storage, and networking configurations to lower operational costs while ensuring performance and reliability. Implement monitoring, observability, and cost-management practices across GCP services. Provide configuration-level and architecture-level recommendations with clear implementation guidance.
• Fraud ML Model Optimization: Support the development, validation, deployment, and optimization of fraud detection machine learning models. Analyze model training and inference workloads to enhance 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.
• ML and Data Pipeline Engineering: Optimize end-to-end machine learning pipelines, including data ingestion, transformation, feature engineering, training, deployment, and inference. Identify bottlenecks and implement solutions to improve throughput and reliability.

Your Skills:

• Experience in GCP engineering with hands-on design and diagnostics.
• Ability to optimize compute, storage, and networking trade-offs to reduce operational costs.
• Experience in implementing monitoring and observability for performance and reliability.
• Familiarity with fraud ML modeling, including building, validating, and deploying models end-to-end.
• Strong skills in optimizing machine learning data pipelines focusing on cost efficiency, performance, and resilience.
• Experience with big data distributed processing, including developing and tuning large-scale batch and stream workloads.
• Proficiency in software engineering, particularly in Python and SQL, with experience in CI/CD and automated testing.

Benefits:

• Hybrid working model allowing flexibility between company offices, client sites, and home.
• Access to extensive training and development opportunities, including over 250,000 courses and certifications.
• Support for employee wellbeing through initiatives like trained Mental Health Champions and wellbeing apps.
• Recognition as one of the World’s Most Ethical Companies for 13 consecutive years.

Degree Requirement: Not Specified

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