Professional Overview
I’m a Python Backend & AI Integration Engineer focused on building reliable systems between APIs, databases, business workflows, and AI services. I work with Python, FastAPI, REST APIs, SQL, OpenAI, and database-backed workflows to build integrations, data pipelines, and AI-assisted automation. My experience includes transforming inconsistent external data into validated and normalized application records, API and webhook integrations, structured LLM outputs, deduplication and idempotency, workflow state, and human-review boundaries for consequential actions. I’m particularly well suited to projects involving API integrations, backend automation, data ingestion and normalization, AI workflow implementation, and custom engineering behind automation systems. My approach is reliability-first: keep deterministic business rules deterministic, use AI where it adds value, validate model outputs, preserve uncertainty rather than guessing, and build workflows that are inspectable and maintainable. I’m available for freelance, contract, and part-time engagements, including scoped implementation projects and ongoing technical support.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built FastAPI services connecting APIs, databases, and AI workflows
- Built pipelines for validation, normalization, deduplication, and storage.
- Integrated OpenAI with structured outputs, validation, and human review.
- Built Supabase/Postgres workflows with SQL migrations and state tracking
- Designed approval-gated automation for consequential external actions.
Key Achievements / KPIs
- Built integrated B2B, AI decision, provider, and data workflows.
- Developed 196 backend test functions across 19 test files.
- Implemented 28 Supabase/Postgres database migrations.
- Built provider-neutral ingestion with explicit review and failure states.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built FastAPI services connecting APIs, databases, and AI workflows
- Built pipelines for validation, normalization, deduplication, and storage.
- Integrated OpenAI with structured outputs, validation, and human review.
- Built Supabase/Postgres workflows with SQL migrations and state tracking
- Designed approval-gated automation for consequential external actions.
Key Achievements / KPIs
- Built integrated B2B, AI decision, provider, and data workflows.
- Developed 196 backend test functions across 19 test files.
- Implemented 28 Supabase/Postgres database migrations.
- Built provider-neutral ingestion with explicit review and failure states.
Areas of Expertise
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
