AI Engineer

Hybrid- travel to the central London office

A specialist investment firm is looking for a hands‑on AI Engineer to build and deploy production‑grade AI systems that directly support their research and investment workflows.

About the role

You will work closely with portfolio managers and analysts to design, build and deploy reliable AI‑driven tools that raise the quality, speed and consistency of their investment processes. Ownership from early prototype through to production deployment, monitoring and ongoing iteration.

What you’ll do

Equity research tooling

• Analyse, summarise and extract structured data from filings, transcripts, external research and internal notes
• Automate screening, idea generation and other repetitive research tasks
• Build internal APIs and lightweight UIs that make these tools genuinely usable for the investment team

System ownership

• Take full‑stack system ownership from prototype to production
• Integrate LLMs and other models with proprietary research data, third‑party market data feeds and existing analyst workflows
• Design and implement RAG systems across internal research, filings and datasets

Governance & collaboration

• Implement practical model risk controls and maintain robust, auditable systems
• Partner with investment professionals to surface and prioritise high‑value use cases

What we’re looking for

• Proven track record building, deploying and maintaining end‑to‑end LLM applications including prompt engineering, API integration and agentic orchestration frameworks
• Hands‑on experience with at least one major cloud platform (AWS, GCP or Azure)

AI & LLM expertise

• Experience with LLM APIs such as OpenAI or Anthropic
• Solid understanding of retrieval‑augmented generation (RAG), embeddings and vector databases
• Knowledge of hallucinations risk and migration strategies
• Experience with SQL and ability to build and maintain data pipelines

Nice to have

• Prior experience applying AI in financial services, investment research or another regulated environment
• Experience with financial data sources – filings, market data, earnings calls, sell‑side research and transcripts

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