MBN Solutions
Senior Manager - AI Engineer (Banking Transformation)

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Senior Manager – AI Engineer (Banking Transformation) AI & Data | Financial Services | Technology & Transformation London (Hybrid) £80,000-£93,000 + Benefits
A leading professional services organisation is seeking a Senior Manager – AI Engineer to join its AI & Data Financial Services practice, focused on delivering large-scale AI transformation across Banking.
This role sits at the forefront of enterprise AI engineering, architecture, and delivery leadership, helping major financial institutions design, build, and operate scalable AI systems that modernise core business processes.
You will operate across the full AI lifecycle—from strategy and architecture through to production deployment and optimisation of agentic AI systems—driving measurable business value through advanced machine learning and generative AI.
This is a senior leadership role combining hands-on technical credibility with programme leadership, stakeholder influence, and team development.
Key Responsibilities
Translate senior stakeholder vision into AI transformation strategies, architecture, and delivery roadmaps Lead and oversee multi-disciplinary AI engineering teams and workstreams Design and deliver enterprise-scale AI systems, including agentic and GenAI solutions Collaborate with architects, data scientists, DevOps, and business stakeholders to define end-to-end solutions Evaluate and select AI technologies (open-source and commercial) and define enterprise deployment patterns Lead design of API-based AI services and scalable backend systems (e.g. FastAPI) Ensure robust integration of AI systems into complex banking and capital markets environments Establish and govern evaluation frameworks for AI and agent-based systems Oversee CI/CD, MLOps, and LLMOps practices across delivery teams Work closely with security, risk, and compliance teams to ensure ethical and regulated AI delivery Own and contribute to architecture reviews, governance forums, and design approvals Engage senior client stakeholders and shape proposals, bids, and AI solution strategies Lead capability development across teams, mentoring senior and junior engineers
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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.
Required Skills & Experience Strong background in software engineering or data engineering with applied AI (Python, SQL) Proven experience delivering AI/ML and generative AI systems in production Deep understanding of LLMs, including: Prompt engineering Embeddings Fine-tuning Retrieval-Augmented Generation (RAG) Demonstrated experience building and scaling agentic AI systems Strong experience with AI system design, architecture, and distributed systems Expertise in API-based backend development (e.g. FastAPI or similar) Experience with vector databases (e.g. Pinecone, Chroma) Experience with agent frameworks (e.g. LangChain, LangGraph, or similar) Strong understanding of evaluation frameworks for AI/agent systems Experience implementing CI/CD pipelines and modern engineering practices Exposure to MLOps / LLMOps principles Experience working with at least one cloud hyperscaler (AWS, Azure, GCP, or Databricks) Strong Agile delivery experience (Agile, SAFe, XP, Jira, Confluence, etc.) Proven ability to lead technical programmes and cross-functional teams Strong stakeholder management and client-facing leadership capability


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Desirable Experience Experience in Banking or Capital Markets (strong preference) Exposure to MCP (Model Context Protocol) Experience operating in regulated enterprise environments Ability to contribute to ROI modelling, business cases, and AI value articulation Experience contributing to bids, proposals, and go-to-market activity
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