Senior AI Engineer, Private Bank

The Investment Products & Solutions division is seeking a Senior AI Engineer to architect and operationalize the Generative AI infrastructure for our Private Banking unit.

We are transitioning our AI initiatives from experimental research to robust, production-grade systems. The primary objective of this role is to build a governed, deterministic content generation engine that enhances the advisory capabilities of our Private Bankers.

You will partner directly with the Head of Digital Advisory (a CFA charterholder and domain subject matter expert) to translate complex financial requirements into secure technical architectures.

 

Core Responsibilities
* System Architecture & Orchestration: Design and implement scalable workflows using LLM frameworks (e.g., LangChain) to automate the production of high-stakes financial documents, including Investment Pitchbooks and CIO Research summaries.
* Systematic Prompt Engineering: Establish a version-controlled Prompt Management System. You will treat prompts as code, implementing rigorous testing frameworks to ensure outputs adhere to specific "House View" tonality and factual accuracy.
* Governance & Safety: Design technical guardrails to mitigate model hallucinations and ensure compliance with Model Risk Management (MRM) standards. You will implement Retrieval-Augmented Generation (RAG) architectures to ground generation strictly in proprietary internal data.
* Production Engineering: collaborate with Enterprise IT teams to integrate AI microservices into existing banking infrastructure, ensuring low latency, high availability, and auditability.
Technical Environment

Our environment prioritizes stability, security, and auditability over experimental flexibility.
* Languages: Python 3.9+ (FastAPI/Flask).
* Frameworks: LangChain, LlamaIndex, OpenAI/Azure OpenAI APIs, Hugging Face.
* Infrastructure: Docker, Kubernetes, CI/CD pipelines for model/prompt deployment.
* Data retrieval: Vector Database integration (e.g., Pinecone/Milvus) for RAG implementations.

Candidate Profile
1. Engineering Rigor (6-10+ Years Experience)
You possess a strong background in software engineering with a recent, specialized focus on deploying Large Language Model (LLM) applications in production environments. You distinguish between "prototyping" and "engineering," valuing reproducibility, logging, and error handling.
2. Financial Domain Literacy
While you are a technologist first, you appreciate the nuance of financial markets. You understand that in Wealth Management, accuracy is a fiduciary responsibility. You are capable of distinguishing between different asset classes and understanding the gravity of investment advice.
3. Commitment to Governance
You view compliance not as an obstacle, but as a critical design constraint. You are experienced in implementing "Human-in-the-Loop" systems and automated validation checks to prevent toxicity or data leakage.
4. Autonomy & Technical Leadership
Reporting to a non-technical domain expert, you will be expected to define the technical standards and architectural choices for the unit. You must be capable of working independently and justifying your engineering decisions based on trade-offs between cost, latency, and accuracy.