Lead AI Engineer - Agentic AI, RAG, LLM - Wealth Management

Rothstein Recruitment; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Rothstein Recruitment is seeking a Lead AI Engineer to own the development and deployment of Agentic AI and RAG-enabled solutions for a Wealth Management client in London. You will lead junior engineers and engage with senior stakeholders to translate business problems into scalable AI architectures.

The role requires hands-on experience in Generative AI, agentic patterns, and collaboration with non-technical stakeholders to drive measurable business value.

Qualifications

• Typically 7+ years in software/AI engineering with hands-on Generative AI
• Proven ability to take complex AI problems from discovery to production
• Advanced Python development and REST API experience
• Strong practical understanding of LLM engineering and agentic patterns
• Ability to translate business problems into technical designs and roadmaps

Responsibilities

• Lead discovery with business teams to identify AI opportunities and success measures
• Architect and build Generative AI solutions, including agents and multi-step workflows
• Design agent behaviour: tool calls, memory, human-in-the-loop controls, and error handling
• Embed security, privacy, governance and least-privilege access by design
• Deliver production-grade apps using Python, APIs and enterprise services
• Collaborate with IT, Data, Cyber and EA colleagues on infrastructure and security

Skills

Python

LLM engineering

Agentic AI patterns

RAG & retrieval

REST APIs

CI/CD / DevOps

Security / privacy by design

Stakeholder communication

Tools

Azure AI/Foundry

Azure OpenAI

LangGraph

LangChain

Semantic Kernel

LlamaIndex

Git

Docker

FastAPI

Job description

Lead AI Engineer - Agentic AI, RAG, LLM - Wealth Management

Excellent opportunity opens for a Lead AI Engineer to join a leading Wealth Management firm in London. You will own the development and deployment on Agentic and Generative AI Solutions, leading junior engineers and working closely with senior stakeholders across the business.

The successful candidate will have excellent hands on experience developing Generative and Agentic AI solutions and the ability to collaborate with non-technical stakeholders.

Key responsibilities:

• Lead discovery with business teams to identify, assess and prioritise AI opportunities, understanding the underlying workflow, information sources, decisions, pain points, risks and measures of success rather than simply accepting stated requirements
• Translate ambiguous business problems into clear technical approaches, solution designs, user journeys, acceptance criteria and delivery plans
• Architect and build Generative and Agentic AI solutions, including agents, multi-step workflows, retrieval-augmented generation (RAG), enterprise knowledge solutions, model integrations and AI-enabled applications
• Design agent behaviour including tool and function calling, orchestration, state and memory, human-in-the-loop controls, hand-offs, retries, error handling, permissions and appropriate boundaries
• Make pragmatic design decisions between deterministic software, workflow automation and agentic approaches, selecting the simplest architecture that meets the business need safely and effectively
• Build and integrate production-quality applications using Python, APIs and enterprise services, working with structured and unstructured data and integrating with existing business systems
• Design effective RAG and enterprise search solutions including document ingestion, chunking, embeddings, vector and hybrid search, metadata filtering, re-ranking, citation and grounding, and access-control-aware retrieval
• Establish evaluation approaches and acceptance criteria for probabilistic AI systems, including golden datasets, deterministic tests, LLM-as-judge techniques, groundedness, relevance, completeness, task completion, tool-call accuracy, retrieval quality, safety and regression testing
• Embed security, privacy, responsible AI and governance controls into solutions by design, including least-privilege access, identity, data classification, secrets management, auditability, traceability, human oversight and protection against prompt injection and malicious inputs
• Lead solutions from rapid prototype through controlled pilot, production deployment, monitoring and ongoing optimisation, using appropriate software engineering, DevOps and CI/CD practices
• Implement observability and production monitoring for AI systems, including quality, latency, cost, failures, model/tool behaviour and user feedback
• Work closely with IT, Data, Cyber and enterprise architecture colleagues on infrastructure, integrations, identity, data foundations, security controls and production engineering standards
• Partner with Risk and Compliance to ensure AI solutions operate within the firm's regulatory requirements, policies and risk appetite, escalating areas requiring specialist review
• Run workshops, demonstrations and working sessions that help colleagues understand what AI can do, identify opportunities and redesign processes around new capabilities
• Act as a technical leader within the AI team, setting reusable patterns, engineering standards and development practices and providing coaching and peer review to other engineers
• Maintain awareness of developments in models, tooling, agent frameworks and engineering practices, assessing new capabilities pragmatically rather than adopting technology for its own sake
• Contribute to the firm-wide AI agenda by identifying new opportunities, demonstrating measurable business value and helping build confidence in safe AI adoption

Experience:

• 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

AI Engineer AI Developer Lead Senior Agentic AI Artificial Intelligence Generative AI GenAI Gen AI Agentic Agents RAG Retrieval Augmented Generation LLM Large Language Model Large Language Models LangChain LangGraph CI/CD Containerisation Python SQL RestAPI Rest API Wealth Management Asset Management Investment Banking Investment Bank Bank Banking Financial Services