AI-Native FullStack Engineer
I build scalable and production-ready AI native applications, including backend, frontend and devops pipelines. Passionate about clean code, modern technologies, and ensuring AI systems are reliable for the user
Hey! I'm Daniel, an AI engineer who builds machine learning systems that actually work in production.
7+ years turning complex ML problems into scalable solutions. From fraud detection algorithms to AI chatbots, I've shipped systems serving millions of users across London, Madrid, and now Abu Dhabi.
Currently architecting AI solutions at Liquidity Capital, leading everything from RAG implementations to serverless ML deployments. I've managed teams, sat at executive tables, and know the difference between research code and production-ready systems.
I'm passionate about the entire ML lifecycle — from business problem to deployed model. When I'm not wrangling databases or optimizing inference pipelines, I'm probably exploring the latest in MLOps tooling or mentoring the next generation of ML engineers.
Leading production lifecycle of AI-native applications including chatbot development with RAG solutions (LangChain) and agentic frameworks. Architecting serverless AI solutions using AWS Lambda and managing multiple databases (ElasticSearch, Neo4J, MongoDB, Postgres).
Developed automated Data Platform for ETL pipelines using Terraform, Python, and AWS ECS. Established production deployment strategy for LLM models with ONNX optimization and TritonServer deployment. Enhanced cloud security for production environments.
Executive Management team member managing AI team of 5 Data Scientists and engineers. Responsible for AI pipeline production deployment as microservices (FastAPI, LLMs, CI/CD) and infrastructure management (Kubernetes, GCP, Terraform).
Delivered end-to-end ML projects for clients in production using Kubernetes. Led ML projects using NLP techniques (Spacy, transformers, ONNX) with gRPC APIs. Established ML Engineer career program and Python coding standards company-wide.
Developed algorithms for in-vitro fertilisation outcomes prediction and computer vision optimization for IVF process. Managed data strategy and vision, deployed ML API services using Django REST and AWS Cloud with Terraform.