AI Engineer

About

The Role We are actively building diverse teams and welcome applications from everyone.

Role: AI Engineer

Location:

Birmingham (SCC operate hybrid working, which comprises of a mix of office and home working)
Contract Type:
Permanent

Salary Package:

Competitive base salary plus large company benefits, a broad flexible benefits scheme, and 2 paid-for volunteering days a year

Hours:

9.00 am – 5.30 pm Monday – Friday Interview Process: 3-stage process

Why SCC?

An inclusive workplace

Excellent package: solid basic and company benefits

Hybrid working & core hours in line with role requirements

Career development and life-long learning opportunities

Opportunity to join Europe's largest privately-owned IT Company Role purpose:
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. Security clearance or willingness to apply will be required for this role.

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

Skills and experience:

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
• Experience building and shipping more than one type of AI system

• Strong proficiency in Python and REST API development
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…