Software & AI Tech Lead · CRR3/RWA Programme · Natixis Investment Managers
Oct 2024 - Present · ParisCRR3 implementation within the risk-weighted assets (RWA) platform: end-to-end ownership of two regulatory-credit workflows; industrialization of the market-risk integration; then design and production delivery of the AI copilot across all three workflows. The platform covers 8 investment portfolios.
Phase 1 · CRR3/RWA programme: solution engineering and market-risk industrialization
Designed and implemented the regulatory-credit capabilities that introduced CRR3 into the RWA application.
- Designed and built an audited credit-inventory workflow covering ingestion, normalization, historical matching, counterparty identification and data-quality controls, with reproducible checkpoints, rollback and validated enriched outputs.
- Built the internal and securitized look-through reporting workflow, taking exposures through enrichment, contracts, outstanding amounts and securities to auditable regulatory deliverables.
- Delivered the application end to end in .NET 8, from workflow and data-processing services to the Razor and SignalR user experience, backed by EF Core and SQL Server.
- Brought a specialist Python market-risk workflow into the platform behind a FastAPI service, defining the .NET/Python boundary, orchestration and failure isolation while leaving the quantitative calculations under the specialist component's ownership.
- Took the solution across development, UAT and production on IIS, with AD/LDAP access control, environment-specific configuration, automated build, test and deployment pipelines, and correlated Serilog logs.
Phase 2 · AI engineering: internal copilot in production across existing workflows
Designed, built and deployed an internal copilot serving three business workflows and 8 investment portfolios, integrated into the existing platform and delivered through its CI/CD pipeline.
- Built a RAG knowledge system over internal SharePoint documentation and technical and functional specifications: ingestion and extraction with Azure AI Document Intelligence, semantic chunking, embeddings and a vector store, with mandatory citation of the source passage. Users get a sourced answer instead of searching across scattered documents.
- Enabled on-demand natural-language analysis over production data: translate the business question into an analytical query, execute it through strictly SELECT-only access to the production API, then explain the result. Analysts can interrogate their portfolios without raising a development request.
- Guided users through the five stages of the credit-inventory workflow and explained the reporting pipeline from enrichment through securities, invoking the existing deterministic services.
- Applied curated tools, human review, accuracy and faithfulness evaluations, guardrails and production observability. The model plans and explains; deterministic services retain control of workflow state and financial calculations.