Senior AI Engineer

Here at Car Finance 247, we are looking for an experienced AI Engineer to join our expanding Technology team and help build production AI services that improve how customers, colleagues and partners move through the car finance journey. As a AI Engineer, you will take a leading role in solving complex, high-impact challenges across the automotive finance industry. You will design, build and deploy production-grade AI solutions, including LLM-powered virtual agents, agentic AI systems and intelligent automation that improve the customer finance journey and support better operational outcomes.

Reporting to the Director of Technology, you will help keep Car Finance 247 at the forefront of AI and emerging technology. You will work closely with stakeholders across the business to identify where AI can deliver the greatest value, shape ideas into practical solutions, rapidly develop proof of concepts, and take successful initiatives through to secure, scalable and fully operational services.

This is a hands-on role with the opportunity to influence our AI strategy, establish engineering best practices and deliver intelligent products that create measurable value for both our customers and the business. You will work across customer-facing journeys and internal operational workflows, turning promising AI ideas into secure, observable and maintainable services. The role suits someone who enjoys hands-on engineering, rapid experimentation and taking responsibility for the path from prototype to production.

Key Responsibilities AI Solution Development:
Design, build and deploy production-grade AI solutions using modern LLM and agentic AI frameworks such as Lang Chain, Lang Graph and Microsoft Foundry, working closely with stakeholders to identify high-value use cases across the business. AI Platform & API Engineering:
Lead the development of scalable APIs and services to host AI and machine learning capabilities, managing the integration, maintenance and continuous improvement of workloads across Azure. Cross-Functional Delivery:
Collaborate closely with Data Analysts, Data Engineers, QA Engineers, Software Developers and other Technology teams to deliver complex projects efficiently, adapting technical approaches to wider platform, product and business needs. End-to-End Project Ownership:
Take ownership of AI initiatives from initial discovery and concept through proof of concept, production deployment and ongoing optimisation, with a strong focus on solving core business problems and delivering measurable value. Continuous

Innovation: Stay at the forefront of developments in generative AI, LLMs, agentic systems and machine learning, assessing emerging tools and techniques and translating their potential for both technical and non-technical audiences. Planning & Delivery Management:
Plan future initiatives and manage active projects through effective sprint planning, backlog prioritisation and ticket management using tools such as Jira. Executive Communication:
Communicate effectively with the Executive Board and senior stakeholders, clearly articulating technical challenges, proposed solutions, delivery risks, project timelines and key milestones. Engineering Standards & Operational Excellence:
Champion best practices across software engineering, testing, observability, security, model evaluation and responsible AI, ensuring AI services are reliable, scalable and maintainable in production. About You 4 years' experience in AI Engineering, Machine Learning Engineering, Data Science or a closely related role, with demonstrable experience delivering production systems. Strong proficiency in Python and Node.js/Type Script, with experience developing both AI/ML models and backend services.

Hands-on experience developing applications using Large Language Models (LLMs), including prompt engineering, structured outputs, tool calling, retrieval-augmented generation (RAG) and evaluation techniques.

Experience with modern LLM and agentic AI frameworks such as Lang Chain, Lang Graph and Microsoft Foundry or equivalent technologies. Strong understanding of software engineering best practices, including testing, code review, CI/CD, dependency and package management, and maintainable system design. Experience deploying and operating AI and machine learning services in production, including API-based architectures and cloud-hosted workloads. Practical experience with MLOps and AI lifecycle management, including data, model and environment versioning, automated deployment pipelines, monitoring and model retraining workflows.

Experience leading complex technical projects from initial discovery and proof of concept through to production deployment and ongoing optimisation. Strong communication skills, with the ability to explain complex AI concepts, technical challenges and delivery considerations to both technical and non-technical stakeholders. A degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics or…