AI Engineer

McCabe & Barton London Area, United Kingdom (Hybrid)

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AI Engineer - Agentic AI & LLM Solutions

Leading Investment House | London/Hybrid | Contract | Competitive Day Rate

About the Opportunity

We are seeking an experiencedAI Engineer to join a leading global investment house embarking on an ambitiousAI transformation programme.

This is a high-impact contract role focused on buildingend-to-end agentic AI and LLM-based solutions that solve real business problems across trading, operations, research, and front-office functions. You'll work directly with business stakeholders to understand workflows, design intelligent automation solutions, and rapidly prototype working AI systems that deliver measurable value.

Key Responsibilities:

AI Solution Design & Delivery

1. Buildend-to-end agentic AI and LLM-based solutions from concept to deployment
2. Design AI architectures that map toreal business problems in investment banking
3. Rapidly prototype and iterate AI solutions based on stakeholder feedback
4. Move quickly from business brief to working solution - velocity is critical
5. Own delivery independently with minimal supervision

Business Engagement & Requirements:

1. Engage directly withbusiness stakeholders (traders, analysts, operations, research teams) to understand workflows and pain points
2. Translate business requirements intoAI solution designs
3. Demonstrate AI capabilities and educate stakeholders on art-of-the-possible
4. Gather feedback and iterate solutions based on real user needs
5. Communicate technical concepts to non-technical business audiences

Technical Implementation:

1. Develop robustPython-based AI applications and agent systems
2. Integrate LLM capabilities (OpenAI, Anthropic, Azure OpenAI) into business workflows
3. Build agentic AI systems that can reason, plan, and execute multi-step tasks
4. Implement RAG (Retrieval-Augmented Generation) pipelines for domain-specific knowledge
5. Work with vector databases and enterprise data sources
6. Integrate AI solutions with existing .NET/C# enterprise systems where required

Innovation & Best Practices

1. Stay current with rapidly evolving LLM and agentic AI landscape
2. Recommend appropriate AI frameworks and tools for different use cases
3. Establish best practices for responsible AI deployment in regulated environment
4. Balance innovation speed with security and compliance requirements

Essential Skills & Experience

AI & LLM Expertise

1. Proven experience building end-to-end agentic AI or LLM-based solutions in production environments
2. Deep understanding ofLLM capabilities and limitations - knows when AI is (and isn't) the right solution
3. Experience designingAI solutions that map to real business problems, not just technical demos or proof-of-concepts
4. Track record ofdelivering working AI solutions that create business value

Technical Skills

1. Strong Python development skills - production-quality code, not just notebooks
2. Ability to architect and buildcomplete AI applications end-to-end
3. Experience integrating AI capabilities into existing enterprise systems
4. Understanding ofsoftware engineering best practices for AI systems

Desirable Skills & Experience

LLM & AI Frameworks

1. Experience with specificLLM providers (OpenAI, Anthropic, Azure OpenAI)
2. Familiarity withagent frameworks such as LangChain, LlamaIndex, AutoGen, or similar
3. Experience buildingmulti-agent systems and orchestration workflows
4. Knowledge ofprompt engineering and optimization techniques

Technical Depth

1. C# / .NET background for enterprise integration in financial services
2. Experience withRAG pipelines and vector databases (Pinecone, Weaviate, ChromaDB, etc.)
3. Understanding ofembedding models and semantic search
4. Knowledge offine-tuning and model customization approaches