Backend APIs
FastAPI and Django services with clear contracts, authentication, data models and deployment-aware structure.
Python engineering for backend, automation and AI systems
Move Python work from promising script to reliable system.
I build Python services and automation around real operational needs: dependable APIs, background processing, data movement and AI-assisted workflows. Existing prototypes are assessed honestly and hardened only where the business case supports it.
APIs, automation and AI integration
Production-minded delivery
Direct senior ownership
75+software projects delivered
10+years solving business systems
1technical owner from start to release
Python systems I deliver
Backends and workflows designed to run repeatedly—not just once.
The emphasis is on validation, failure recovery, visibility and maintainable operations around the core Python logic.
Business automation
Repeated manual processes converted into traceable jobs with approvals, retries and useful exception handling.
AI-enabled workflows
LLM and model APIs integrated with grounding, structured output, evaluation points and human control where it matters.
Data processing
Imports, transformations and synchronisation pipelines built to expose bad input rather than quietly corrupt downstream data.
Prototype hardening
Useful experiments given configuration, tests, security boundaries and deployment foundations suitable for real users.
Reliability improvements
Failing jobs, slow endpoints and opaque production behaviour traced through logs, metrics and focused code changes.
Ways to start
Match the engagement to the uncertainty in the system.
A defined API can be scoped directly; automation and inherited prototypes often benefit from a smaller validation step first.
Python Build
An API, internal service or processing workflow delivered against a clear operational outcome.
- Technical boundaries documented
- Milestone implementation
- Automated checks for critical paths
- Deployment and operating notes
Automation Sprint
A bounded engagement to validate and implement one high-value process before expanding the automation programme.
- Current workflow mapped
- Risks and exceptions identified
- Working end-to-end slice
- Evidence for the next investment
Backend Ownership
Reserved engineering time for Python services that need regular changes, monitoring and production continuity.
- Planned improvement backlog
- Incident and defect analysis
- Dependency and security updates
- Capacity for new integrations
Need a defined block of senior development time?
View current capacity and hour packages
Engineering fit
Automation succeeds when the messy cases are part of the scope.
I work best with teams willing to expose how the process really behaves, including exceptions, manual decisions and imperfect source data.
There is a valuable workflow and an accountable owner.
- The current process and desired outcome can be demonstrated
- Source systems provide legitimate access or APIs
- Someone can decide how exceptions should be handled
- A controlled first release is preferable to a giant automation promise
The plan assumes Python or AI will remove every human decision.
- No one can explain or own the current process
- Success cannot be observed or measured
- The solution depends on unauthorised scraping or data access
- A prototype is expected to become production software overnight
International client experience
Remote collaboration shaped by real client work.
I have worked directly with clients in the United States, Canada, the United Kingdom, Germany and other international markets. For roughly the past six years, most of my client work has been with US-based businesses—across time zones, established team workflows and direct stakeholder communication.
United StatesCanadaUnited KingdomGermanyInternational
Python delivery stack
Boring foundations around the clever parts.
PythonFastAPIDjangoFlaskPostgreSQLCeleryRedisPydanticPytestLLM APIsDockerLinux
Python project questions
What to establish before automating anything important.
The first discussion focuses on the workflow, data, failure cost and operating environment rather than beginning with a preferred library.
Often, yes. I first review what the script assumes about input, state and execution, then add the API, validation, security, testing and operational controls the real usage requires.
Yes. I integrate model services into defined workflows and include structured outputs, grounding, evaluation and fallback paths according to the risk of the use case.
Yes. I can add features, improve architecture or diagnose production issues after establishing how the application runs, deploys and stores data.
I can prepare containerised deployments, CI/CD and practical logging or metrics. The exact infrastructure responsibility is agreed based on your hosting and internal ownership.
Possibly. I map the decisions, data quality problems and exceptions first; sometimes a small controlled tool is more appropriate than replacing the entire workflow.
When technical or data uncertainty is high, I propose a paid validation sprint with a concrete question and stopping point before estimating a production implementation.
Show me the workflow as it really runs
Build Python automation the business can rely on.
Explain the current process, its inputs and what happens when it fails. I will identify the unknowns and recommend either a focused validation step or a delivery plan.