Selected engineering work

Real systems. Real engineering constraints.

These anonymised case studies describe work and projects from my professional experience. Client names, proprietary details and confidential implementation specifics are omitted. I have deliberately avoided inventing performance metrics or business outcomes where verified figures are not available.

01 / Fintech & payments

Engineering transaction platforms and payment integrations

Context

Payment products must coordinate transaction state, network or processor interfaces, business rules and operational recovery. My professional work has included payment switches, wallet systems, a credit card engine, jPOS and integrations in payment environments.

Engineering challenge

The central challenge in this class of system is preserving correct transaction behaviour across distributed components and external dependencies—including timeouts, reversals, duplicate messages, settlement and reconciliation.

Engineering focus

  • Payment message and interface handling using jPOS, including ISO 8583 and ISO 20022 contexts
  • Transaction lifecycle and state management
  • Service integration and system boundaries
  • Failure handling, reversals and reconciliation concerns
  • Reliability and operational considerations
What this demonstrates

Hands-on experience in the engineering domain of transaction processing, payment switches, cards and wallets. For a new engagement, the architecture and implementation plan are derived from the client's product, partner contracts, controls and constraints—not copied from a previous system.

02 / Applied AI & developer tooling

Building an AI-native Frappe development operator with human approval

Context

I developed a custom AI-assisted Frappe development operator with human approval controls. The goal was to apply AI to software engineering work inside an existing application-development context.

Engineering challenge

Giving an AI system access to engineering tasks is not simply a model-selection problem. Tool execution, scope boundaries, reviewability and human control have to be considered as part of the system design.

Engineering focus

  • Integration of AI-assisted task execution into a Frappe-oriented engineering workflow
  • Task-oriented agent execution
  • Human-in-the-loop approval checkpoints
  • Controlled execution rather than unrestricted autonomy
  • Connecting AI capabilities to a real developer workflow
What this demonstrates

A concrete implementation exploring AI-assisted engineering with explicit human approval. It informs how I assess agent use cases, execution boundaries and operational controls in new software and business workflows.

03 / Enterprise platforms

Frappe multi-site platform engineering

Context

My professional experience includes Frappe multi-site platform work and custom application development. Multi-site environments introduce concerns beyond an individual application: tenant or site boundaries, deployment structure, configuration, upgrades and ongoing operations.

Engineering challenge

The platform design has to account for how sites are provisioned and maintained, how custom applications are shared or separated, and how changes can be deployed without creating unnecessary coupling or operational risk.

Engineering focus

  • Frappe application and platform structure
  • Multi-site deployment considerations
  • Custom application and extension boundaries
  • Operational maintainability and upgrade planning
  • AI-assisted development workflows in the Frappe context
What this demonstrates

Practical experience with Frappe beyond isolated customisations, including platform-level and multi-site concerns. A new client's architecture still needs to be validated against its tenancy model, operating team and deployment requirements.

04 / Performance & scalability

Application Performance and Scalability Engineering

Problem space

As applications grow, bottlenecks can emerge across service boundaries, database access, APIs and asynchronous processing. In transaction-heavy environments, performance issues can affect reliability as well as throughput.

Engineering approach

Investigate critical request and processing paths, examine service interactions and data access patterns, and identify constraints before selecting optimisation strategies. Recommendations are grounded in workload characteristics and operational requirements.

Engineering focus

  • Backend and API performance
  • Database query and data-access patterns
  • Microservices and event-driven processing
  • Transaction throughput and failure paths
  • Scalability and reliability trade-offs
Relevant experience

Backend, payment-switch, wallet and distributed application engineering provides a foundation for investigating performance and scalability challenges in unfamiliar systems.

05 / Engineering platforms & developer experience

Engineering Platform and Developer Experience

Problem space

Development teams can lose time to repetitive setup, fragmented tooling and manual engineering workflows. Inconsistent environments and deployment practices also make delivery harder to reproduce.

Engineering approach

Identify friction across the development lifecycle and design reusable tooling, automation and workflow foundations around the team's actual stack. Where AI assistance is appropriate, introduce it with bounded execution and review controls.

Engineering focus

  • Developer tooling and workflow automation
  • CI/CD and repeatable delivery foundations
  • Development environment management
  • AI-assisted engineering workflows
  • Human approval and execution controls
Relevant experience

Includes building an AI-assisted Frappe development operator with human approval controls, alongside broader platform and engineering automation work.

06 / Architecture & modernisation

System Modernisation Roadmap

Problem space

Legacy dependencies, evolving product needs and operational constraints make modernisation difficult to sequence. Replacing components without understanding system boundaries can introduce avoidable delivery and migration risk.

Engineering approach

Start by understanding the current architecture, dependencies and constraints. Define a target direction, compare viable migration paths and break the work into phases that account for business continuity, risk and implementation capacity.

Engineering focus

  • Current-state architecture and dependency mapping
  • Target architecture definition
  • Legacy integration and migration options
  • Phased implementation planning
  • Technical risk and trade-off analysis
Relevant experience

Enterprise architecture and technical leadership across evolving business systems inform a pragmatic approach to modernisation rather than technology replacement for its own sake.

07 / Cloud & infrastructure

Cloud-Native Platform Engineering

Problem space

Applications need infrastructure and deployment workflows that are repeatable, secure and supportable across environments. Ad hoc provisioning and release processes can increase operational effort and deployment risk.

Engineering approach

Design infrastructure and delivery workflows around application architecture, team capability and operational requirements. Use automation and infrastructure-as-code where they improve consistency and maintainability.

Engineering focus

  • AWS and Azure architecture
  • Infrastructure as Code and Terraform
  • CI/CD and deployment automation
  • Environment and configuration management
  • Operational foundations and maintainability
Relevant experience

Cloud architecture, infrastructure automation and delivery engineering experience across application platforms provides a basis for designing cloud-native foundations suited to each engagement.

08 / Applied AI & software engineering

AI-Powered Software Maintenance

Problem space

Understanding unfamiliar code, investigating defects, maintaining documentation and generating useful tests can consume significant engineering time. AI can assist, but its output must be checked against the codebase and system behaviour.

Engineering approach

Identify bounded maintenance tasks where AI can accelerate analysis or produce a useful first pass. Integrate assistance into existing developer workflows, with validation, code review and human control over changes.

Engineering focus

  • Codebase analysis and navigation
  • Bug investigation assistance
  • Test and documentation generation
  • AI-supported engineering workflows
  • Reviewable, human-controlled execution
Relevant experience

Hands-on exploration and implementation of AI-assisted software engineering workflows, including an operator designed around human approval rather than unrestricted autonomous changes.

09 / Enterprise integration

Enterprise Integration Hub

Problem space

Businesses rely on a connected ecosystem of ERP, CRM, HR, finance, commerce, customer-support and data platforms. When these systems operate in silos, teams face duplicate data entry, inconsistent records, delayed processes and limited operational visibility. Reliable integration must account for data ownership, security, workflow semantics, error handling and recovery—not just endpoint connectivity.

Engineering approach

Map the systems, business processes, data ownership and integration requirements first. Select suitable API, event-driven, messaging or file-based patterns; define data contracts and transformation rules; and design for security, observability, retries, idempotency and operational recovery.

High-value integration scenarios

  • ERP ↔ CRM: synchronise customers, accounts, orders and invoices
  • E-commerce ↔ ERP / inventory: connect orders, stock, fulfilment and returns
  • HRMS ↔ payroll / finance: automate employee, attendance, payroll and accounting flows
  • CRM ↔ customer support: share customer context, cases and service history
  • ERP / operational systems ↔ BI and data warehouses: consolidate reporting and business metrics
  • Identity provider ↔ business applications: centralise SSO, user provisioning and access control
  • Banking, payment and financial platforms: exchange transaction, settlement and reconciliation data
Engineering focus

Enterprise APIs and integrations · Event-driven messaging · Data mapping and transformation · Secure file exchange · Identity and access integration · Monitoring, retries and reconciliation.

Relevant experience

Experience with enterprise application integration, payment interfaces, ISO 8583 and ISO 20022 messaging contexts, and secure file exchange provides a foundation for working across varied enterprise systems. Each integration is designed around the systems, data contracts and operational requirements involved.

10 / ERP & business applications

Custom ERP Engineering

Problem space

Standard ERP functionality does not always reflect how a business operates. Organisations may need custom modules, workflows and integrations while retaining a maintainable foundation that can evolve with the business.

Engineering approach

Understand the operational process and distinguish configuration from genuine custom development. Extend the ERP platform with focused application logic, workflow changes and integrations while considering upgradeability and long-term ownership.

Engineering focus

  • Frappe and ERPNext application development
  • Custom modules and business logic
  • Workflow and process customisation
  • ERP and external-system integrations
  • Multi-site structure and maintainability
Relevant experience

Frappe application development and multi-site platform engineering provide a practical foundation for extending ERPNext and building business-specific ERP capabilities.

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