Ai Engineer

AI Engineer: Mid–Senior Level · Hybrid / UK · Full-Time

Role Overview

We are looking for an AI Engineer to design, build, and maintain AI-powered systems across SCC. This is a broad and evolving role, the nature of the work will vary by initiative. One project may call for an AI agent that orchestrates data sources and tools; another may require a Retrieval-Augmented Generation (RAG) system over internal knowledge; another may be an AI layer embedded into an existing business process or platform.

What stays consistent across all of it is the approach: working with best-in-class foundation models via API, integrating cleanly with enterprise data and systems, and building things that work reliably in production — not just in a demo.

You will work closely with the Data Science team who supply clean, structured data, and with MLOps who handle deployment and infrastructure. You will also engage directly with business stakeholders to understand problems before proposing AI solutions.

Key Responsibilities

· Design and build the appropriate AI solution for each initiative, whether that is an agent, a RAG pipeline, a document processing system, or something else

· Integrate foundation models (via API) into business applications and workflows

· Develop MCP (Model Context Protocol) servers and tool integrations where agent-based approaches are appropriate

· Build RAG pipelines that surface accurate, attributed answers from internal knowledge and data sources

· Transform AI capabilities into APIs and microservices that connect with existing enterprise platforms

· Build prompt engineering frameworks that produce structured, reliable, and auditable outputs

· Implement context management, tool routing, and retrieval patterns that control what the model sees and when

· Collaborate with Data Scientists to ensure clean, trusted data enters every AI system

· Test and validate AI system behaviour across real use cases; iterate based on production feedback

· Ensure all AI outputs are transparent, traceable, and compliant with governance requirements

· Document system behaviour, data flows, tool calls, and decision logic thoroughly

· Work with product and business teams to embed AI into existing workflows

· Support AI adoption and share best practices across SCC

Skill Set

AI System Design

· Selecting the right AI pattern for the problem — agent, RAG, pipeline, classifier, or hybrid

· AI agent architecture and multi-step workflow orchestration

· Retrieval-Augmented Generation (RAG) design and implementation

· Document processing and knowledge extraction systems

· Working with foundation models via API (e.g. Azure OpenAI, OpenAI)

· Prompt engineering for structured, grounded, and reliable outputs

· Context management, chunking strategies, and retrieval optimisation

· Hallucination mitigation and source attribution by design

Software Engineering & Integration

· MCP (Model Context Protocol) server development and tool routing

· Building APIs and microservices to expose AI capabilities

· Integrating AI systems with enterprise tools and data sources

· Secure API integration (OAuth, API keys, credential management)

· Software engineering best practices, testing, versioning, documentation

· Understanding of enterprise data flows and system architecture

Analytical & Governance

· Statistical reasoning sufficient to validate and interpret AI outputs

· Evaluating AI system performance and identifying failure modes

· Understanding of AI risk, bias, and transparency obligations

· Familiarity with AI governance frameworks (e.g. EU AI Act)

· Clear documentation of system behaviour, inputs, outputs, and limitations

Interpersonal & Professional Skills

· Translating AI capabilities into clear business outcomes for non-technical stakeholders

· Cross-functional collaboration with data scientists, MLOps, and product teams

· Ability to push back on AI for its own sake and advocate for the right solution

· Championing best practices and supporting AI adoption across the organisation

Technologies

Python · Azure OpenAI (GPT-4o) · Azure AI Foundry · MCP (Model Context Protocol) · LangChain / LlamaIndex · Vector Databases (e.g. Azure AI Search, Pinecone, Weaviate) · FastAPI · REST APIs · Azure Key Vault · Docker · Prompt Flow · Azure Monitor · Git · SQL

What We Are Looking For

· 2+ years in software engineering, with at least 2 years focused on AI or LLM-powered systems

· Experience building and shipping more than one type of AI system, not just agents, not just RAG

· Strong proficiency in Python and REST API development

· Clear understanding of how foundation models work, where they are useful, and where they fail

· A product-minded engineer who builds for reliability and real-world use, not proof-of-concept demos

· Strong communication skills, able to explain what AI can and cannot do in plain language to any audience

· Comfortable working on initiatives at different stages of maturity simultaneously