- Hong Kong Hong Kong
Capgemini Hiring! Full Time GCP - Machine Learning Optimisation Consultant in Hong Kong - Ricebowl
Working Location
Job Description
Responsibilities
Job Title: GCP & ML Optimisation consultant (Fraud)
Role summary
Outcome-based optimisation assessment , implementation-ready Hrecommendations role, with explicit expectations for hands-on analysis on optimizing fraud Models , ML platform on Google Cloud by reducing run-cost & Improving performance and reliability across GCP infrastructure, ML pipelines .You’ll apply strong engineering discipline and collaborate with IT Infra , data scientists and fraud stakeholders to deliver measurable compute and inference savings.
Produce workable, implementable recommendations with effort/cost savings
Provide configuration-level and code-level guidance
Key responsibilities
GCP engineering: Hands-on design and diagnostics; optimise compute/storage/network trade-offs to reduce run-cost; implement monitoring and observability for cost, performance, and reliability.
Fraud ML modelling support: 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: Optimise pipelines from ingest → transform → feature creation/serving → 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.
Desired (bonus)
Feedzai: Exposure to or implementation experience with Feedzai (integration, performance tuning, operationalisation).
Full-time
Role summary
Outcome-based optimisation assessment , implementation-ready Hrecommendations role, with explicit expectations for hands-on analysis on optimizing fraud Models , ML platform on Google Cloud by reducing run-cost & Improving performance and reliability across GCP infrastructure, ML pipelines .You’ll apply strong engineering discipline and collaborate with IT Infra , data scientists and fraud stakeholders to deliver measurable compute and inference savings.
Produce workable, implementable recommendations with effort/cost savings
Provide configuration-level and code-level guidance
Key responsibilities
GCP engineering: Hands-on design and diagnostics; optimise compute/storage/network trade-offs to reduce run-cost; implement monitoring and observability for cost, performance, and reliability.
Fraud ML modelling support: 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: Optimise pipelines from ingest → transform → feature creation/serving → 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.
Desired (bonus)
Feedzai: Exposure to or implementation experience with Feedzai (integration, performance tuning, operationalisation).
Full-time
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