White Oak is an FCA authorised and regulated European Lending Platform which provides capital to support SMEs and mid-corporates through its full suite of flexible lending products. Headquartered in the UK, White Oak’s team of 170 specialists currently services a loan portfolio of over £500 million and have originated over £1.5 billion of assets since 2018. As a financing affiliate of White Oak Global Advisors, LLC (WOGA), White Oak is able to access investor capital to support White Oak’s lending activities to complement its funding from banks and other financial institutions.
Our values define our corporate culture and reinforce what is important to us. We encourage high standards of behaviour and accountability at all times which make our organisation stronger and more successful. Our Company Values are:
Teamwork; Strong working relationships internally and externally.
Agility
Perseverance; Solutions focused; Flexibility; Innovative.
Respect
Integrity; Ownership; Accountability.
Function and Job Purpose
White Oak has completed its Azure migration and is now embedding agentic AI across the business — from AI-assisted credit underwriting to natural-language querying (NLQ) of our data across Credit, Sales and Marketing.
The Agentic AI Engineer will design, build and run AI agents and the context that powers them — prompts, instructions and reusable agent skills — drawing on the semantic models and business definitions built by the wider data team. Working within our AI governance framework and alongside delivery partners, they will take use cases from requirements through to production, primarily using SnapLogic, Claude Code and our Azure data platform, ensuring every agent is accurate, explainable and auditable.
Role and Key Responsibilities
- Build, test and deploy AI agents and reusable agent skills (e.g. bank statement analysis, transactional history, sales and campaign automation), primarily in SnapLogic via its native tools/Claude Code.
- Design, version and maintain high-quality prompts, system instructions and context, tested against clear acceptance criteria.
- Work with the wider data team to inform the design of semantic models, the business glossary and metric definitions, so they meet the needs of NLQ and agents.
- Connect agents to data and systems via APIs and MCP, including Power BI semantic models and the Azure data warehouse.
- Translate requirements from underwriters, Credit, Sales, Service and Marketing into scoped, testable agent and skill specifications.
- Evaluate agent quality — build test sets, monitor accuracy, hallucination and drift, and iterate.
- Ensure agent outputs are logged, traceable and auditable, following our AI governance and change control (Dev / UAT / Production) processes.
- Work with third-party delivery partners and IT to move proofs of concept into production.
- Document agents, skills and data dependencies so they can be supported, reused and scaled.
- Anything else deemed reasonable by your line manager.
Core Skills and Knowledge
- Hands-on experience building LLM-based solutions or AI agents (tool use, retrieval / RAG, multi-step workflows) in a commercial or substantial project setting.
- Strong prompt and context engineering — structured prompts, examples, output schemas and systematic testing.
- Good understanding of semantic layers and how they ground AI — measures, dimensions, relationships and business definitions (e.g. Power BI / Tabular) — and the ability to specify what agents need from them.
- Good SQL and working knowledge of a cloud data platform (Azure preferred).
- Experience with APIs and integration (REST, JSON) and some scripting (Python preferred).
- Awareness of how to evaluate and govern AI output — accuracy, explainability, auditability and data privacy.
- Ability to turn business needs into clear technical requirements and explain AI capability and limitations to non-technical stakeholders.
- Organised and delivery-focused, comfortable progressing several use cases at once.
Any other attributes that would be helpful, but not essential for the role
- SnapLogic experience, particularly AgentCreator or agentic pipelines.
- Experience with Model Context Protocol (MCP) or agent frameworks (e.g. Claude, Azure AI Foundry, LangGraph, Semantic Kernel).
- Financial services or lending background — credit underwriting, bank statement or bureau data, FCA-regulated environments.
- Knowledge of AI governance standards and regulation (e.g. EU AI Act, ISO/IEC 42001).
- Exposure to CRM and marketing data (Salesforce, HubSpot) and campaign use cases.
- Curious and pragmatic, with a drive to keep pace with fast-moving AI tooling.
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Agentic AI Engineer in Chester employer: White Oak UK
White Oak is an exceptional employer that fosters a collaborative and high-performing work culture, making it an ideal place for ABL Analysts to thrive. With a strong focus on employee growth, we offer comprehensive training and development opportunities, ensuring that our team members are equipped to excel in their roles. Located in a dynamic environment, employees benefit from engaging with industry leaders and participating in exciting marketing initiatives, all while adhering to our core values of Collaboration, Agility, Respect, and Excellence.
