Vetted on a call by a Wave consultant, not scraped. £175,000 and above.
Production AI delivered into healthcare and social care, and they spent five months embedded with the backend engineering team doing RAG modelling and new product development rather than staying on the client side. Python and TypeScript with PostgreSQL and pgvector, vector search, LLM evaluation and agentic frameworks including Mastra, on AWS. Before engineering they did fund accounting and automation at a global asset manager, which is where the financial rigour comes from.
Looking forThe job is drifting towards customer-facing sales work and they don't want to become a salesperson. They want implementation and discovery, staying close to the code and to product development, and to keep building their AI skills.
How they came acrossWas honest without prompting that their hands-on coding has become heavily AI-assisted over the last few months and that they need to refresh the fundamentals. Strong problem-solving instincts and strategic read on where a product should go. Recently promoted, so they're leaving from a position of strength.
Python, TypeScript, SQL · RAG, pgvector, vector search, LLM evaluation · Mastra, agentic frameworks, prompt engineering · AWS, Docker, PostgreSQL, Snowflake
Analytics and automation in healthcare software and institutional finance, a stint as a mathematical prompt engineer, then forward deployed engineering at a non-profit technology company. 8 employers, average tenure 1 year 2 months, longest 3 years 5 months.
They'd be the first to say their unassisted coding needs work, and £175,000 is a big number against 10 years and a non-profit base, so the expectation needs testing early.
Masters in business analytics. Certified Fraud Examiner, and CS50.
Built the entire AI function at a wealth and asset management consultancy on their own, roadmap through to shipped product, with LLM systems running in live workflows across more than 100,000 portfolios. Agentic systems on LangGraph with MCP and FastMCP, RAG with hybrid vector and BM25 search, multi-LLM orchestration, and evaluation frameworks and guardrails built for a regulated environment with PII de-identification and data lineage. Python engineer first, FastAPI and AsyncIO and Pydantic, deployed on Azure Functions and Container Apps.
Looking forThey want AI to be the product rather than a service sitting next to a data platform, and they want the scale that comes with that. Delivery, not advisory, and no legacy maintenance.
How they came acrossA quantitative PhD who works comfortably in loosely-defined problems, which is the actual job in forward deployment. Only passively looking, so they're not in a hurry, and they were specific about wanting client-facing work rather than a back room.
Python, FastAPI, AsyncIO, Pydantic · LangGraph, MCP, FastMCP, RAG, hybrid vector and BM25 · Azure Functions, Container Apps, Azure OpenAI, Docker · pgvector, Chroma, FAISS, SQL, Polars
Academic research in computational physics and cancer research, then senior data science, and now applied AI engineering inside a consultancy. 4 employers, average tenure 1 year 10 months, longest 3 years 1 month.
3 months notice, they're on a contract rather than permanent, and every AI system on the CV they built alone, so there's little evidence of how they work inside a larger engineering team.
PhD in computational physics from a globally top 100 ranked university.
Five years as a senior specialist solutions architect at a data and AI platform vendor, taking enterprise customers from discovery through evaluation and prototype to production LLM deployments. Agentic and multi-agent systems, RAG, model serving, evaluation frameworks and AI governance, built on Spark and MLflow with Python and SQL. They started out as a Hadoop developer and reconciliation engineer, so the data plumbing is first-hand rather than borrowed.
Looking forLeaving the platform vendor this week and available now. They planned 6 months off and changed their mind because they would rather be working. Open that they want to earn well while the AI market is hot, and happy to travel.
How they came acrossMoves between an engineering-level technical conversation and a C-suite business case without changing gear. Came back to us the same day and was clear about what they wanted, which is not universal at this level.
Python, SQL, Spark, MLflow · RAG, agentic and multi-agent systems, evaluation frameworks · Model serving, AI governance · AWS, Azure, Docker
5 employers over 10 years, from capital markets data engineering into consulting at Deloitte and then 5 years at a data and AI platform vendor. Average tenure 1 year 5 months, longest stint 2 years 3 months.
£225,000 sits at the top of this bracket and is driven by market timing rather than a competing offer they named. The last decade has been customer-facing architecture rather than owning a production codebase.
Bachelors in computer science. Certified in AWS solutions architecture, Azure data engineering and generative AI engineering.
Tell me which came closest and I recalibrate, then come back with 10 to 15 more inside 24 hours.
Show me the wider bank