Job Description

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

Other Requirements

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