Professional Overview
AI & backend engineer shipping production RAG, agentic, and LLM systems end to end. Built a traceable RAG copilot and agent flows on FastAPI + Anthropic SDK at a regulated fintech, keeping AI out of the core decision path for auditability. Live project: EU AI Act copilot (aiactcopilot.eu). Python, FastAPI, pgvector, LangGraph.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built advisory AI layer, keeping LLMs out of the core credit decision path
- Built a traceable RAG copilot on Postgres/pgvector with FastAPI + Anthropic SDK
- Built document-extraction pipeline parsing financial docs into structured data
- Own the end-to-end platform: Python/FastAPI, 32 endpoints, 120+ tests, AWS CI/CD
- Built multi-step agent flows (extract, validate, draft) for underwriter review
Key Achievements / KPIs
- Trained ML valuation model, +6.7pp within-10% accuracy over prior method
- 120+ automated tests and insert-only audit model for full traceability
- Built Companies House ingestion + Land Registry/EPC pipeline over ~2.9M records
AI Expertise
Skills
Employment Type
Description of Responsibilities
- L2 production support across 7-8 products on a multi-tenant SaaS platform
- Diagnosed and fixed failures across APIs, databases, and infrastructure
- Fixed failing Airflow DAGs to restore high-throughput batch pipelines
- Monitored service and pipeline health with Airflow logs and New Relic
- Optimised query performance on GCP BigQuery and automated tasks in Python
Key Achievements / KPIs
- Maintained ~99% SLA across core products through proactive monitoring
- Cut recurring-incident resolution from ~2 days to same-day
- Processed ~25 GB and millions of records daily across the platform
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built Python/SQL automation to consolidate data from 35+ retail stores
- Cleaned and transformed sales, inventory, and product data feeds
- Developed backend utilities for e-commerce catalog management
- Built product parsing, attribute tagging, and bulk-upload workflows
- Built Power BI dashboards on sales, top-sellers, and stock-outs
Key Achievements / KPIs
- Automated manual retail reporting into decision-ready dashboards
- Kept data consistent across 35+ stores, reducing manual overhead
Education
Field of Study: Information Systems with Computing
Field of Study: Engineering
Areas of Expertise
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.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
