AI Solutions Engineer Generative AI Agentic AI Python AI Agent Retrieval-Augmented Generation (RAG) LLM Applications LLM Engineering Azure

An exciting opportunity has opened for an AI Engineer to play a crucial 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., * 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, * Shape the technical foundations, engineering standards and development life cycle of a new business-focused AI capability
• Take ownership of high-profile AI use cases from discovery through to production and see their impact across the firm
• Work directly with senior leaders and subject matter experts across investment management, client teams, operations, risk, compliance and corporate functions
• Influence how a regulated wealth-management business adopts Agentic AI safely and at scale
• Remain hands-on with rapidly developing AI technology while also developing broader solution architecture, product discovery and technical leadership skills
• Help build a lasting organisational AI capability

Essential

• Typically 7+ years’ experience in software engineering, solutions engineering, application development or a related technology role, with demonstrable recent hands-on experience designing and delivering Generative AI solutions; equivalent depth of experience will be considered
• Evidence of taking complex solutions from ambiguous business problem through design, build, testing and production deployment
• Advanced Python development skills and strong software engineering foundations, including modular design, code quality, version control, debugging and automated testing
• Strong experience designing and consuming REST APIs, working with JSON and integrating applications with enterprise systems and services
• Strong practical understanding of LLM engineering, including model selection, system instructions, structured outputs, function/tool calling, context management, reasoning models, multimodal models, latency/cost/quality trade-offs and common failure modes
• Advanced experience of agentic AI patterns, including orchestration, state and memory, multi-step workflows, tool use, human-in-the-loop design, permissions, retries and failure handling
• Advanced practical experience of RAG and enterprise knowledge solutions, including embeddings, vector/semantic/hybrid search, retrieval design, re-ranking, grounding and retrieval evaluation
• Strong understanding of AI evaluation and testing methods for non-deterministic systems, including evaluation datasets, regression testing, groundedness/relevance measures, task and tool-call evaluation and production feedback loops
• Strong cloud engineering experience, preferably within Microsoft Azure, and familiarity with services used to host, integrate, secure, observe and operate AI applications
• Experience of Microsoft Azure AI/Foundry and Azure OpenAI or equivalent enterprise AI platforms; familiarity with agent frameworks such as Microsoft Agent Framework, OpenAI Agents SDK, LangGraph, Semantic Kernel, LangChain or LlamaIndex
• Working knowledge of SQL and data handling across structured and unstructured sources
• Experience of Git-based development, CI/CD, containerisation and production deployment practices; familiarity with Docker and application/service frameworks such as FastAPI is highly desirable
• Strong understanding of identity, authentication, authorisation, role-based access control, secrets management, secure development and privacy-by-design principles
• Understanding of AI-specific security and responsible AI risks, including prompt injection, data leakage, malicious inputs, excessive permissions, hallucination, traceability and human oversight
• Experience working in Agile or iterative product/software delivery environments, using rapid prototyping, user stories, acceptance criteria, backlog management and continuous improvement practices
• Excellent business analysis and problem decomposition skills, including requirements elicitation, process mapping, workshop facilitation and translating business needs into technical designs
• Excellent stakeholder management and communication skills, with the ability to work credibly with senior non-technical colleagues and explain complex technical concepts in clear business language
• Strong judgement, curiosity and pragmatism, with the confidence to challenge proposed solutions and recommend a non-AI approach when appropriate
• Proficient use of AI-assisted software development tools such as GitHub Copilot, OpenAI Codex or equivalent, with an understanding of how to verify, test and govern AI-generated code

Preferred

• Experience working in a regulated industry, ideally financial services, wealth management or asset management
• Experience delivering AI solutions involving sensitive, confidential or personally identifiable information
• Experience with Azure AI Search, Azure Functions, App Services, Key Vault, Entra ID, Azure Monitor/Application Insights or equivalent services
• Experience establishing reusable agent patterns, AI engineering standards, evaluation frameworks or technical guardrails across a team
• Experience mentoring engineers or providing technical leadership without moving away from hands-on delivery
• Understanding of model risk, operational resilience, data governance and regulatory expectations relevant to enterprise AI
• Degree-level education in computer science, software engineering, engineering, mathematics or a related discipline is desirable but not essential where equivalent practical experience can be demonstrated