About The Client

The Client is a leading global provider of high-performance network, voice, and data center services, enabling businesses to connect seamlessly across major financial and commercial hubs worldwide.

Business Need:

  • To establish an AI Center of Competency to accelerate AI/ML adoption across business units

  • Needed a scalable architecture for data engineering, ML model lifecycle management, and AI solution incubation

  • Required multi-disciplinary expertise—from solution architecture to model deployment and testing—while ensuring strong governance and documentation

Solution:

  • Defined the AI CoC Charter covering innovation, capability building, governance, GTM enablement, and delivery support.

  • Assembled a cross-functional team:

    • Architect – Design GCP-based AI/ML pipelines (AIOps), ensure architecture governance, and guide development squads on multi-vendor cloud solutions.
    • AI & Machine Learning Engineers – Build supervised/unsupervised models, predictive analytics, NLP, and reinforcement learning solutions.
    • Data Engineering & Pipelines – Develop data lake management and pipelines using SQL, ETL tools, and platforms such as Google BigQuery and Databricks.
    • Cloud Engineers – Implement ML services with GCP VertexAI, Data Fusion, and Dataplex, with robust CI/CD for ML.
    • Testing & Model Lifecycle – Led A/B testing, uplift modelling, and field experiments for targeted marketing effectiveness.

Business Outcome:

  • Accelerated AI adoption: Enabled 40% faster deployment of AI/ML use cases across departments with a repeatable framework.

  • Improved Data-to-Insight Cycle: Reduced model development and deployment time by 50% through automated data pipelines and ML-driven CI/CD processes.

  • Robust Governance & Scalability: Comprehensive documentation and multi-vendor expertise supported future expansion and compliance.

  • Business Impact: Enhanced decision-making for marketing, operations, and customer experience through accurate predictive and prescriptive insights.

Tools & Technologies:

GCP (Vertex AI, BigQuery, Data Fusion, Dataplex), Databricks, CI/CD for ML, SQL/ETL frameworks, A/B testing & uplift modeling tools.

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