Rothstein Recruitment
Senior AI Solutions Engineer - Transformation - Wealth Management
London
Posted about 23 hours ago
Get notified of more jobs like this · No spam, ever
How your CV stacks up
1Upload CV
2Analyse CV
3Improve CV
Upload your CV to see how well it fits this job role
?%
Senior AI Solutions Engineer - Transformation - Wealth Management
An exciting opportunity for a Senior AI Solutions Engineer. Shape the AI capability of a leading wealth-management business. In this role, you will take complex business challenges from discovery and architecture through to production, building Generative and Agentic AI solutions that deliver measurable impact. If you’re an experienced AI engineer who wants to combine deep technical expertise with genuine business influence, technical leadership and the opportunity to shape how AI is adopted safely at scale, this is a role to make your mark.
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
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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
- 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
Get help with your application
Your very own career expert that helps elevate your application to the next level.
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
Opportunities:
- Shape the technical foundations, engineering standards and development lifecycle 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
AI Solutions Engineer | Generative AI | Agentic AI | Python | AI Agents | Retrieval-Augmented Generation (RAG) | LLM Applications | Azure AI | Engineering | Wealth Management | Fast API
Trusted by 25,000+ job seekers
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Location