AI Solutions Engineer - RAG - Python - LLM - Wealth Management

AI Solutions Engineer - RAG - Python - LLM - Wealth Management

An exciting opportunity has arisen for an ambitious, hands-on AI Solutions Engineer to play a key role in shaping how Generative AI is used across a leading wealth-management environment. You'll take real business challenges and turn them into practical, production-ready AI solutions, working across RAG, LLMs, Agentic AI and Python to create applications that genuinely improve how teams work. You will work closely with business users and colleagues across AI, IT, Data, Cyber, Risk and Compliance, taking ownership from discovery and rapid prototyping through to testing, deployment and continuous optimisation.

Key responsibilities:

• Build, test, deploy and maintain Generative and Agentic AI applications that address defined business problems and operate within agreed architecture, security and governance standards
• Work directly with users and subject matter experts to understand workflows, gather requirements, clarify acceptance criteria and iterate solutions based on feedback
• Develop agents and multi-step AI workflows using model APIs, structured outputs, tool/function calling, state and memory, human-in-the-loop controls, retries and error handling
• Build RAG and enterprise knowledge solutions, including document ingestion, chunking, embeddings, vector/semantic/hybrid search, metadata filtering, grounding and citations
• Integrate AI solutions with internal systems, APIs, data sources and enterprise services using Python and appropriate application frameworks
• Develop reliable tools and functions for agents to interact with approved systems, applying appropriate authentication, authorisation, validation and permissions
• Create and maintain automated tests and AI evaluation sets, helping assess groundedness, relevance, task completion, tool-call accuracy, retrieval quality, safety and regression
• Use rapid prototyping to test assumptions early, then strengthen successful prototypes into supportable production solutions with appropriate logging, monitoring and documentation
• Apply secure development and responsible AI practices, including data handling, least privilege, secrets management, input/output validation, auditability and protection against prompt injection and inappropriate data exposure
• Use Git, code review, CI/CD and containerisation practices to develop and release software consistently and safely
• Monitor production solutions for quality, failures, latency, cost and user feedback and contribute to ongoing optimisation
• Work collaboratively with IT, Data and Cyber colleagues on integrations, infrastructure, identity, deployment and production support
• Support the Senior AI Solutions Engineer in solution design, technical standards, evaluation practices and reusable development patterns
• Demonstrate solutions to business users and explain how they work, their limitations and how they should be used safely

Essential:

• Typically 3+ years' experience in software engineering, application development, data/AI engineering or a related role, with demonstrable hands‑on experience building Generative AI applications; equivalent capability and practical experience will be considered
• Strong Python development skills and solid software engineering fundamentals, including clean code, debugging, modular design, source control and automated testing
• Practical experience using LLM APIs and building applications with system instructions, structured outputs, function/tool calling and context management
• Hands‑on experience building agentic workflows or AI agents and understanding the difference between deterministic workflow logic and model‑driven behaviour
• Practical experience of RAG, embeddings and vector/semantic search, including document ingestion, retrieval and grounding responses in source material
• Good experience integrating applications through REST APIs and working with JSON, authentication and enterprise data/services
• Working knowledge of SQL and handling both structured and unstructured data
• Experience with a cloud platform, preferably Microsoft Azure, and familiarity with enterprise AI services such as Azure AI/Foundry, Azure OpenAI or equivalent
• Familiarity with at least one modern agent or LLM application framework, for example Microsoft Agent Framework, OpenAI Agents SDK, LangGraph, Semantic Kernel, LangChain or LlamaIndex
• Understanding of AI evaluation and testing, including creating representative test cases, regression testing, checking grounding/relevance and assessing whether an agent completed the intended task correctly
• Experience using Git and collaborative software development practices; working knowledge of CI/CD, containers such as Docker and application/service frameworks is desirable
• Awareness of secure software development, identity and access management, secrets handling, privacy, data leakage and common AI risks such as prompt injection and hallucination
• Experience working iteratively with users using Agile or similar delivery practices, including rapid prototyping, user stories, acceptance criteria and continuous improvement
• Proficient use of AI‑assisted software development tools such as GitHub Copilot, OpenAI Codex or equivalent, combined with disciplined review, testing and verification of generated code

Preferred:

• Experience with Microsoft Azure services such as Azure AI Search, Functions, App Services, Key Vault, Entra ID and Azure Monitor/Application Insights or equivalent services
• Experience of building applications that use sensitive, confidential or personally identifiable information within controlled environments
• Experience with FastAPI or similar Python application/service frameworks
• Exposure to evaluation tooling, observability platforms or techniques for monitoring LLM/agent behaviour in production
• Experience in financial services, wealth management, asset management or another regulated industry

Opportunities:

• Build real AI products and agents that move beyond experimentation into day‑to‑day business use
• Work across a wide range of business areas and develop a strong understanding of how a wealth‑management firm operates
• Develop specialist skills in Agentic AI, RAG, evaluation, AI security and enterprise deployment
• Learn directly from a senior AI engineer while retaining meaningful ownership of design and delivery
• Gain exposure to senior stakeholders and high‑profile business transformation initiatives
• Grow with a new AI capability as its technology, operating model and portfolio of use cases expand

AI Solutions Engineer Generative AI Agentic AI Python AI Agents Retrieval-Augmented Generation (RAG) LLM Applications AI Engineering Wealth Management Fast API

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