IT/AI Infrastructure Support

Modernizing financial services demands a careful balance between rapid innovation and uncompromising security. I worked directly with a leading client in the Financial Services sector to modernize their legacy IT environment into a scalable, cloud-native AI platform.

Rather than operating as a delivery team, I engaged with the client as an individual technical lead—overseeing their internal engineering teams, setting architectural direction, and guiding execution across DevOps, ML, and AI functions. The client faced infrastructure bottlenecks that severely constrained model training and experimentation. I designed and led the migration to Google Cloud Platform (GCP), establishing a secure, high-performance MLOps ecosystem optimized for PyTorch and enhanced with Google Gemini’s multimodal capabilities. The result was an AI-ready platform capable of real-time financial analysis and advanced predictive modeling, while fully adhering to strict regulatory and compliance requirements.

The Challenge: An Innovation Bottleneck

The client had developed sophisticated financial models in PyTorch but lacked the infrastructure required to train, deploy, and iterate on them at scale. Their on-premise environment was resource-constrained, resulting in model training cycles that stretched into weeks.

In parallel, leadership wanted to adopt Generative AI to analyze unstructured data—such as market news and earnings transcripts—but had no secure or compliant framework for integrating tools like Google Gemini. They needed a modern, end-to-end AI platform and strong technical leadership to bridge the gap between data science ambition and operational reality.

The Solution: A Cloud-Native AI Ecosystem

I led a phased transformation, working hands-on with the client’s engineering teams while overseeing architecture, security, and delivery standards.

I designed the target cloud architecture and guided the client’s DevOps teams through implementation:

Infrastructure as Code: Defined the entire GCP environment using Terraform to ensure reproducibility, auditability, and elimination of configuration drift.

Security by Design: Established granular IAM policies, VPC Service Controls, encrypted networking, and data isolation aligned with financial regulatory requirements.

Automated Provisioning: Introduced CI/CD pipelines for infrastructure and networking, reducing environment provisioning from days to minutes.

With the secure foundation in place, I led the build-out of the AI and MLOps layer:

Optimized PyTorch Training: Architected scalable GPU-enabled training clusters on GCP Compute Engine to dramatically accelerate PyTorch-based financial models.

End-to-End MLOps: Implemented Vertex AI to manage the full model lifecycle—data ingestion, training, deployment, monitoring, and rapid retraining for time-sensitive trading use cases.

Google Gemini Integration: Designed and supervised the implementation of secure API gateways enabling Gemini to analyze unstructured data (news, earnings calls, reports). This enriched quantitative models with real-time qualitative insights while maintaining compliance and data governance.

90% Reduction in Deployment Time: Model iterations moved from development to production in hours instead of weeks.

Advanced Market Intelligence: Generative AI (Google Gemini) became part of daily analytical workflows, unlocking deeper insight from unstructured data.

Scalable, Compliant Architecture: A fully audited, auto-scaling AI platform that meets financial regulatory standards and supports future innovation.