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About

My academic background is in economics and quantitative finance, including option pricing and financial time-series analysis. I combine technical direction with hands-on implementation. As Engineering Lead at DQC, I work on platform architecture and core product features while building the engineering organization.

From 1 January 2027, I will work independently, supporting defined projects and ongoing technical engagements. How we can work together.

Selected delivery experience #

Product development and engineering leadership at DQC #

I help turn product ideas and proofs of concept into production software for enterprise data quality. My work includes core Python features, agent workflows, model orchestration and platform architecture. I also strengthened the backend and deployment infrastructure and built the engineering organization through hiring, onboarding and mentoring.

Quantitative finance and research systems at 2Xideas #

I built a backtesting engine for investment strategies and trading simulation, a centralized market-data repository and internal Python packages for quantitative research. I turned research code into production software, improved risk-modeling and asset-management products and modernized existing systems in a regulated investment environment. Earlier work at msg for banking covered financial time-series forecasting, model validation and market-risk analysis.

Applied machine learning at anacision #

I built a web application and optimization algorithm for personnel planning, developed automotive predictive-maintenance models using terabytes of data and worked on data APIs and prediction services. I also implemented generative models for differentially private synthetic data.

Technical capabilities #

  • Backend architecture and modernization: Design Python services, APIs and database integrations. Evolve existing systems, migrate dependencies and turn research code or prototypes into maintainable production software.
  • AI integration and orchestration: Provision and integrate models through services such as Amazon Bedrock, Google Cloud Vertex AI and Microsoft Foundry on Azure. Build agent workflows, context enrichment and model routing with governed access across providers.
  • Data engineering and applied ML: Build data pipelines, external data integrations and distributed processing for large datasets. Connect statistical models, forecasting and optimization to the applications that use them.
  • Cloud infrastructure and delivery: Develop deployment infrastructure on AWS and Azure using Kubernetes and automated delivery pipelines. Support cloud migrations, batch processing and interactive workloads.

Outside work, I enjoy trail running, chess, cooking and hiking.