Job Summary

Job Description: AI Lead Engineer (Wealth Data Platform)

Key Responsibilities

Key Responsibilities

• Natural Language Democratisation: Develop and deploy Text-to-SQL and Text-to-Insight interfaces that allow non-technical Wealth Managers to interact with the conformed data layer using LLMs.
• Ontology & Knowledge Graph Engineering: Design and implement a domain-specific Wealth Ontology. Graph databases (e.g., Neo4j or Snowflake Relational Graphs) need to be leveraged to map complex client relationships and financial hierarchies that standard SQL fails to capture.
• Agentic Workflows: Build and orchestrate Autonomous Agents (using frameworks like LangGraph, ADK, CrewAI, or AutoGen) capable of executing multi-step financial reasoning such as automated portfolio rebalancing checks or proactive client insight generation.
• Modern Data Alignment: Ensure all AI models are integrated into the SageMaker Unified Studio and adhere to the bank’s OBDQ standards to prevent "hallucinations" in regulated client reporting.
• Productivity Tooling: Work with Analytics Engineers to embed LLM-based chatbots into front-line tools to reduce manual data gathering time for client-facing staff.

Skill Requirements

Technical Requirements

• AI/ML Foundations: Deep expertise in LLM orchestration (RAG), Fine-tuning, and Prompt Engineering.
• Graph Technology: Experience building Ontologies or using Graph-based RAG to improve the retrieval of structured/unstructured wealth data.
• Data Stack: Proficiency in Python and SQL. Familiarity with Snowflake (Cortex), AWS SageMaker, and Kafka for real-time agent triggers.
• Engineering Rigor: Experience with LLMOps (monitoring, evaluation, and versioning) within a highly regulated Banking (FCA/PRA) environment.

• ✅ Mandatory YES/NO filters:

  • ✅ Python + SQL (strong hands-on)
  • ✅ LLM experience (RAG + Prompt Engineering)
  • ✅ Built GenAI/LLM applications (real projects)
  • ✅ PyTorch or TensorFlow
  • ✅ Data engineering exposure
  • ✅ Cloud (AWS/Snowflake)

  ✅ Strong preference:

  • ✅ Agentic AI frameworks
  • ✅ Knowledge graph / Neo4j
  • ✅ Kafka / real-time systems
  • ✅ LLMOps / MLOps
  • ✅ Banking / regulated domain

Other Requirements