Sr AI Engineer

professionals an intelligence advantage over the market. Underneath sits a structured data platform on Snowflake and AWS

covering commercial property transactions, tenant occupancy, availability, investor activity and financial KPIs — with a semantic

layer that lets agents query this data through natural language. The candidate will design and ship multi-agent workflows end to

end: orchestrating LLM tool-calling across data sources, building retrieval and generation pipelines for document intelligence, and

connecting agents to the surfaces where brokers work — from lease expiry targeting and heads of terms review to client pitch

generation, investment matching and market monitoring. The role owns the full lifecycle from scoping use cases with business

SMEs through to production deployment on cloud infrastructure in wave-sequenced sprints.

Must-have Skills

LLM Agent Development — designing multi-agent systems

with tool-calling, chain-of-thought reasoning, structured outputs

and context management using Claude, OpenAI or Azure

OpenAI

Agentic Orchestration — building agent graphs and pipelines

(LangGraph, LangChain or equivalent); routing, disambiguation,

fallback handling and multi-step task decomposition

Python (5+ yrs) — async, Pydantic, FastAPI; building tool

interfaces, API services and data pipelines for production agent

systems

RAG & Document Intelligence — retrieval-augmented

generation, document extraction, summarisation and content

generation for business cases, client decks and reports

Cloud & Data Platforms — AWS (S3, Lambda, Bedrock,

SageMaker, Glue, Step Functions) and/or Snowflake; deploying

and scaling agent workloads on cloud infrastructure

SQL & Semantic Modelling — complex joins, CTEs, star

schemas; designing semantic layers that ground LLM agents in

structured data with verified queries and NL-to-SQL pipelines

Agent Evaluation & Guardrails — accuracy benchmarking,

hallucination detection, output validation, human-in-the-loop

review patterns and safety guardrails

Enterprise Integration — connecting agents to CRM,

SharePoint, Teams, email and document management systems

where business users work

NICE TO HAVE

Commercial real estate or professional services domain

knowledge

AWS Bedrock / SageMaker for LLM hosting and fine-tuning

MCP (Model Context Protocol) server design

Snowflake Cortex Analyst / semantic views

RAG pipeline design (Pinecone, Weaviate, FAISS)

Agent observability (LangSmith, Arize, Phoenix)

AWS CDK / CloudFormation for infrastructure-as-code

Agentic UX design for non-technical users

TECH STACK

Claude API OpenAI API LangGraph LangChain AWS

Snowflake Python FastAPI Pydantic Bedrock

SageMaker S3 Lambda Glue MCP Protocol Cortex

SQL Pinecone Weaviate RAGAS LangSmith Docker

K8s GitHub Actions

Interested in building AI for UK Advisory

Candidates should submit a CV, brief cover note, and links to any LLM agents, agentic workflows, or demos they've built