Production evidence

Proof from systems that had to work.

Selected engagements across trading, investment, risk, and regulated operations—organized around the constraint, the engineering intervention, and the outcome delivered.

Discuss a production constraint
Selected work
6 engagements
Operating context
Live, regulated, measurable
Disclosure
Outcomes shown without client identities

Evidence portfolio

From production constraint to measurable outcome.

Filter by operating environment or engineering discipline. Each summary keeps technology in service of the decision and result.

Energy & Trading Trading systems

Energy trading infrastructure built for market speed

Constraint

A live energy-trading platform needed lower latency, resilient market connectivity, and enough observability to operate continuously.

Engineering intervention

Reengineered the platform around non-blocking event flows, certified EPEX M7 connectivity, structured telemetry, and explicit recovery patterns.

Outcome evidence

  • 99.9% uptime after eliminating blocking I/O bottlenecks
  • Sub-2ms feed-handler and order-processing latency
  • 80% reduction in manual intervention

Energy & Trading · Trading systems

Energy trading infrastructure built for market speed

Constraint

A live energy-trading platform needed lower latency, resilient market connectivity, and enough observability to operate continuously.

What changed

Conducted comprehensive code review and platform reengineering to achieve enterprise-grade scalability. Built async-first architecture with asyncio and RabbitMQ Streams for non-blocking I/O. Implemented structured logging at scale with Loguru and JSON serialization. Integrated EPEX and M7 Interface for real-time market feeds, iTrade REST APIs for order execution, and Azure microservices for cold-path analytics. Deployed resilient patterns with circuit breakers, exponential backoff, and heartbeat monitoring.

Delivery scope

  • — End-to-End Code Review & Remediation
  • — Async-First Architecture - asyncio and RabbitMQ Streams
  • — Market Feed Integration - EPEX and M7 Interface
  • — Structured Logging at Scale
  • — Real-Time Performance and Application Monitoring
  • — Hybrid Architecture - Azure microservices with hot-path trading logic

Outcome evidence

  • — 99.9% uptime after eliminating blocking I/O bottlenecks
  • — Sub-2ms feed-handler and order-processing latency
  • — 80% reduction in manual intervention
Discuss a similar project
Pharma & Health Care Data products

Global sales and marketing analytics platform

Constraint

A global commercial team needed one usable analytics product across fragmented sales, marketing, and management data.

Engineering intervention

Built harmonized data pipelines and a focused Plotly Dash product on Azure, with repeatable delivery through containers and CI/CD.

Outcome evidence

  • More than 800 users across sales, marketing, and management
  • One analytics product adopted across all global business units
  • Reusable drill-through analysis for churn, margin, cross-selling, and long-tail questions

Pharma & Health Care · Data products

Global sales and marketing analytics platform

Constraint

A global commercial team needed one usable analytics product across fragmented sales, marketing, and management data.

What changed

Leveraged Dash / Plotly, Azure, Spark, pandas, polars, and PySpark. Emphasized CI/CD with GitLab, containerization, and AKS. Delivered focused dashboards in Plotly Dash, integrated with Synapse Analytics and Azure SQL databases.

Delivery scope

  • — Large-scale analytics pipelines (pandas, polars, PySpark)
  • — Plotly Dash + Predictive Sales & Marketing Analytics
  • — Generalized and harmonized analyses for multiple drillthroughs, e.g. churn, cost passthrough, margin outlier, cross-selling, longtail, etc.

Outcome evidence

  • — More than 800 users across sales, marketing, and management
  • — One analytics product adopted across all global business units
  • — Reusable drill-through analysis for churn, margin, cross-selling, and long-tail questions
Discuss a similar project
Banking & Financial Services Governance and controls

BAIT and DORA compliance governance

Constraint

A banking control process depended on slow, manual greylist generation, classification, and regulatory reporting.

Engineering intervention

Introduced governed Python APIs and scalable classification workflows that integrated with the bank's existing data interfaces.

Outcome evidence

  • Regulatory reporting aligned with BAIT requirements
  • Reduced false positives and weeks of manual classification work
  • Automated quarterly reporting saved days of work

Banking & Financial Services · Governance and controls

BAIT and DORA compliance governance

Constraint

A banking control process depended on slow, manual greylist generation, classification, and regulatory reporting.

What changed

Developed RESTful Python APIs for dynamic greylist generation. Deployed HPC-based processes for data processing, scan classification and compliance checks, seamlessly integrated with existing enterprise systems and data interfaces.

Delivery scope

  • — API-based BAIT Greylist Generator (Flask APIs)
  • — BAIT compliance & automated reporting
  • — Seamless integration with Big Data/AI teams
  • — CI/CD with GitLab, Ansible

Outcome evidence

  • — Regulatory reporting aligned with BAIT requirements
  • — Reduced false positives and weeks of manual classification work
  • — Automated quarterly reporting saved days of work
Discuss a similar project
Asset Management Investment platforms

Front-office investment platform

Constraint

Front-office teams had to replace a fragmented legacy estate while meeting KAIT, model-governance, and audit deadlines.

Engineering intervention

Formed investment-engineering teams and introduced a governed Azure, Python, and domain-oriented platform architecture.

Outcome evidence

  • More than 200 billion in assets managed through the platform
  • Five strict audit deadlines met
  • Adopted by multiple investment teams

Asset Management · Investment platforms

Front-office investment platform

Constraint

Front-office teams had to replace a fragmented legacy estate while meeting KAIT, model-governance, and audit deadlines.

What changed

Introduced agile teams with 'investment engineers' bridging finance and software. Leveraged Azure cloud, Python-based HPC pipelines, and domain-driven microservices to meet regulatory and performance requirements.

Delivery scope

  • — Replaced 50+ legacy apps to comply with KAIT regulations
  • — Azure, Python, Docker, microservices
  • — Met 5 strict audit deadlines

Outcome evidence

  • — More than 200 billion in assets managed through the platform
  • — Five strict audit deadlines met
  • — Adopted by multiple investment teams
Discuss a similar project
Asset Management Risk engineering

Quantitative risk reporting platform

Constraint

Risk teams were spending too much time assembling hundreds of reports from disconnected analytical and operational systems.

Engineering intervention

Unified the reporting pipeline across existing C++, C#, MATLAB, Excel, and database logic, then automated the recurring production flow.

Outcome evidence

  • 90% reduction in report generation time
  • Risk-monitoring cycle moved from monthly to daily
  • Hundreds of monthly reports automated

Asset Management · Risk engineering

Quantitative risk reporting platform

Constraint

Risk teams were spending too much time assembling hundreds of reports from disconnected analytical and operational systems.

What changed

Focused on HPC-based data aggregation. Streamlined advisor collaboration with integrated C++/C# modules, Excel macros, and MATLAB analytics. Reduced manual overhead and improved ETL processes.

Delivery scope

  • — Unified risk reporting for multiple mandates and investment strategies.
  • — Hundreds of monthly reports automated
  • — Forward-looking simulations & aggregated risk
  • — Integrated C++/C# logic, Excel, MATLAB, MSSQL

Outcome evidence

  • — 90% reduction in report generation time
  • — Risk-monitoring cycle moved from monthly to daily
  • — Hundreds of monthly reports automated
Discuss a similar project
Asset Management & Risk Scientific computing

Economic scenario generation at production scale

Constraint

ALM and risk teams needed large stochastic scenario sets without waiting for serial simulations or losing model transparency.

Engineering intervention

Engineered calibrated Monte Carlo solvers with GPU acceleration, comparative model analysis, and interfaces for expert forecasts.

Outcome evidence

  • More than 100x faster scenario generation vs. traditional methods through GPU acceleration
  • Published research at Euro-Par 2010 on HPC-based scenario generation
  • Foundation for production ALM and risk modeling systems

Asset Management & Risk · Scientific computing

Economic scenario generation at production scale

Constraint

ALM and risk teams needed large stochastic scenario sets without waiting for serial simulations or losing model transparency.

What changed

Engineered a comprehensive economic scenario generator (ESG) for Asset-Liability Management (ALM) applications. Implemented Monte Carlo-based stochastic differential equation solvers with GPU acceleration (CUDA) for parallel simulations. Integrated expert forecasts through sophisticated calibration techniques. Developed comparative analysis framework evaluating ESG models across multiple scenarios. Full implementation with C++/C# for performance-critical paths, MATLAB for financial algorithms, and Excel/VBA for business user interfaces.

Delivery scope

  • — GPU-Accelerated Monte Carlo Simulations (CUDA)
  • — Expert Forecast Calibration Techniques
  • — Multi-Model Comparative Framework
  • — Stochastic Differential Equation Solvers
  • — Large-Scale Scenario Generation (100k+ paths)
  • — High-Performance Computing Stack (C++/C#, MKL, ILNumerics)

Outcome evidence

  • — More than 100x faster scenario generation vs. traditional methods through GPU acceleration
  • — Published research at Euro-Par 2010 on HPC-based scenario generation
  • — Foundation for production ALM and risk modeling systems
Discuss a similar project

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