We have partnered with a high-growth SaaS business that has moved well beyond the AI experimentation phase. AI is embedded directly into their core platform, powering features that are live in production and relied upon by real users every day. They are now looking for an Applied AI Engineer to help raise the bar on what those features can do.
This is a hands-on role for someone motivated by outcomes. You will work at the intersection of engineering, product and AI, turning real user problems into production-grade features that ship, perform and improve over time.
About the role:
- Build and ship AI-powered features embedded directly into customer-facing products, owning them from prototype through to production
- Design and implement end-to-end AI workflows including RAG pipelines, embedding models, vector search and retrieval strategies optimised for real-world accuracy and scale
- Apply techniques such as LLM integration, prompt engineering, chain-of-thought prompting and agentic patterns including tool use and multi-step reasoning
- Integrate AI components cleanly into existing backend services and product workflows, holding AI features to the same engineering standards as the rest of the codebase
- Evaluate model performance, latency, cost and reliability in live environments, making trade-offs that balance user experience with operational efficiency
- Iterate rapidly based on user feedback and production behaviour, treating AI features as living systems rather than one-time deliverables
About you:
- Strong engineering background with a proven track record of shipping AI-powered features into production
- Deep proficiency in Python including API development, service integration and building maintainable systems at scale
- Hands-on experience with LLMs in live environments, including prompt design, model integration and output evaluation
- Solid understanding of embeddings, vector databases such as Pinecone, Weaviate or pgvector, and retrieval-based approaches
- Familiarity with orchestration frameworks such as LangChain or LlamaIndex and agentic system design
- Strong grasp of production trade-offs across latency, cost, reliability and security in AI systems
