Senior AI Engineer

Location: Greater Manchester (Hybrid, 2 - 3 Days On-Site)

We're working with an established fintech business in Greater Manchester that is doing something genuinely interesting with AI right now. Not pilots. Not proof of concepts gathering dust. Real, production-grade AI embedded into customer journeys and internal operations, and they're only just getting started.

They've recently brought in a Head of Data and AI who has both the vision and the organisational backing to push this forward hard. A small team of agentic AI engineers is already in place. This hire sits right at the heart of that team.

This is a hands‑on senior engineering role focused entirely on applied AI. Model research and pure data science sit elsewhere. This is about taking foundation models, LLM services, orchestration frameworks, and existing ML capabilities and turning them into robust, production‑ready solutions that work at scale in the real world.

Just as importantly, you'll be a genuine voice for AI across the business, helping technical and non‑technical colleagues alike understand what this technology can actually do.

• Designing and building production‑grade AI application patterns that bring together LLMs, retrieval systems, tools, APIs, and internal platforms into end‑to‑end solutions
• Building and improving agentic AI capabilities across customer‑facing and internal operational use cases, with a sharp focus on reliability, resilience, latency, and observability
• Owning AI architecture decisions and helping the team choose the right approach across vendor capabilities, orchestration frameworks, and integration methods
• Translating emerging AI capabilities into practical implementations, knowing where agentic workflows, tool use, RAG, and LLM‑based decision support genuinely add value
• Establishing and improving engineering practices across orchestration patterns, testing, evaluation, monitoring, guardrails, and release
• Educating and upskilling colleagues across the business, from engineers to operational teams, on what AI can do and how to work with it well

• A strong software engineering background with excellent Python skills and solid discipline across code quality, testing, version control, and deployment
• Proven hands‑on experience integrating LLMs, AI services, or ML components into production applications
• Hands‑on experience with agentic AI frameworks, orchestration libraries, and workflow engines
• Strong working knowledge of application‑layer AI patterns: tool use, workflow‑based agents, RAG, structured outputs, fallback handling, and human escalation
• Experience designing robust API‑driven integrations between AI capabilities and wider engineering platforms and business systems
• A solid grasp of production AI concerns: latency, scalability, resilience, observability, regression testing, and safety controls
• Experience working in a governed or regulated environment where traceability and risk‑awareness matter

• AWS‑based AI and ML services and their integration into broader platform architecture
• Experience in financial services or another regulated sector
• Building conversational, assistant‑style, or autonomous workflow experiences
• Contributing to the wider AI or engineering community through talks, writing, or open source