Full Stack Software Engineer (AI/ML Platform)
Location: London (Hybrid/On-site as needed)
Rate: Up to £650 per day
Contract: Outside IR35
Positions: 2 roles available
We're working with a fast-growing AI scale-up in London that's building next-generation machine learning platforms used at serious scale. They're looking for two experienced Full Stack Software Engineers to help design, build, and scale production-grade AI-driven systems.
This is a hands-on contract role at the intersection of modern Front End engineering, Back End systems, and applied machine learning.
What you'll be working on
- Building high-performance, user-facing applications using React and TypeScript
- Designing and developing scalable Python backends powering ML-driven products
- Integrating machine learning models into real-world production systems
- Working on data pipelines, inference services, and model-serving APIs
- Collaborating closely with ML engineers, data scientists, and product teams
- Improving system performance, reliability, and scalability in a fast-moving environment
- Shipping features quickly while maintaining clean, testable, production-quality code
Core tech stack
Frontend
- React (modern hooks-based architecture)
- TypeScript
- Component-driven UI design
- Performance-focused Front End engineering
Backend
- Python (FastAPI/Flask/Django-style architectures)
- REST & event-driven APIs
- Asynchronous and concurrent systems
AI/ML & Data
- Model inference pipelines
- ML model integration (not just notebooks)
- Data-heavy, Real Time or near-Real Time systems
- Exposure to tools such as PyTorch, TensorFlow, or similar (hands-on or integration-level)
Cloud & Engineering
- Cloud-native architecture (AWS/GCP/Azure)
- Containerisation (Docker, Kubernetes)
- CI/CD and infrastructure-as-code
- Observability, monitoring, and logging built-in from day one
What they're looking for
- Strong experience as a Full Stack Software Engineer
- Deep knowledge of React + TypeScript in production environments
- Strong Python Back End engineering experience
- Proven ability to work in high-growth, fast-paced teams
- Comfortable working close to ML systems, data pipelines, or AI products
- Engineering mindset - pragmatic, scalable, and delivery-focused
Nice to have (but not required)
- Experience building ML-powered products end-to-end
- Exposure to LLMs, embeddings, vector databases, or model serving frameworks
- Background in data engineering or platform engineering
- Experience in AI, fintech, healthtech, or deep-tech environments