Redcentric
Architect - AI Solutions
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Aim of the role:
The AI Solutions Architect will shape and enable practical AI adoption across Redcentric’s product and service portfolio, creating customer-facing offerings and internal capabilities that improve service quality, automation, productivity, and operational efficiency.
The role combines AI, data, automation, security, governance, and modern delivery expertise with strong architectural leadership. It will translate AI opportunities into secure, governed, repeatable, and commercially viable capabilities that can be delivered and operated at scale.
A key focus is embedding AI into existing products, services, operational capabilities, and delivery practices, while also developing specific AI product offerings, internal tooling, process automation, knowledge enablement, and engineering accelerators.
Key responsibilities:
AI Strategy, Architecture, and Governance
- Shape Redcentric’s AI strategy, roadmap, and capability development plan, identifying practical opportunities across customer services, internal operations, automation, and knowledge management.
- Design secure, scalable, and supportable AI solutions, translating business objectives into reusable architectures, delivery patterns, and implementation guidance.
- Ensure AI adoption is governed, responsible, and supportable, with appropriate controls for data protection, security, access, risk, auditability, and operational ownership.
AI Product and Service Development
- Develop AI-enabled product and service offerings that enhance Redcentric’s existing portfolio and can be delivered repeatedly and commercially.
- Define offering scope, service components, delivery stages, support model, commercial assumptions, customer value proposition, and route from pilot to production.
- Create reusable assets such as service definitions, reference architectures, implementation guides, risk controls, and customer-facing technical content.
Internal Enablement and Delivery Excellence
- Identify and deliver opportunities for AI-enabled productivity, process automation, operational improvement, and knowledge enablement.
- Support internal tooling, assistants, agents, and workflows that reduce manual effort, improve quality, and increase operational consistency.
- Promote repeatable, controlled, and secure delivery practices across AI initiatives, including testing, validation, documentation, and service transition.
Customer, Commercial, and External Engagement
- Act as a trusted advisor to customers, supporting discovery, use-case prioritization, solution design, and controlled adoption of AI capabilities.
- Provide technical leadership for AI-related bids, proposals, workshops, commercial opportunities, and customer engagements.
- Maintain awareness of emerging AI capabilities, operating models, governance approaches, and market trends to inform Redcentric’s propositions and delivery approach.
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This list of responsibilities is not exhaustive, and the role holder is expected to reasonably take on any other responsibilities required to support business activities within the Redcentric Group.
Success Measures
A successful AI Solutions Architect will:
- Deliver repeatable AI architectures, delivery patterns, and implementation frameworks.
- Define and mature specific AI product offerings with clear service, commercial, and operational models.
- Embed AI into existing Redcentric services and internal operating capabilities.
- Improve automation, documentation, knowledge management, engineering productivity, and operational efficiency.
- Support secure, governed, and supportable AI adoption through modern delivery and assurance practices.
- Contribute to pipeline growth, customer satisfaction, service innovation, and technical differentiation.
Person specification
The ideal candidate will be able to demonstrate the following skills and experience:
Professional Attributes
A successful AI Solutions Architect will:
- Turn emerging AI concepts into practical, supportable, and repeatable capabilities.
- Take a portfolio-minded approach to enhancing existing services rather than treating AI as a standalone technology.
- Collaborate effectively across Product, Architecture, Engineering, Security, Assurance, Service Delivery, Operations, and Commercial teams.
- Communicate complex AI concepts clearly to technical and non-technical audiences.
- Balance innovation with governance, risk, customer trust, commercial viability, and operational supportability.
- Show curiosity, pragmatism, and a strong bias toward measurable outcomes, reuse, and continuous improvement.
Technical Expertise
The successful candidate should demonstrate strong knowledge across the following broad areas:
Artificial Intelligence & Automation
- Generative AI, foundation models, large language models, AI assistants, agents, and conversational interfaces.
- Retrieval Augmented Generation, enterprise search, knowledge assistants, prompt lifecycle, orchestration, and workflow automation.
- AI-enabled service management, documentation, knowledge management, use-case prioritization, responsible AI, and governance.
Platform and Service Architecture
- Platform, service, and solution architecture principles, including connectivity, integration, dependency mapping, and interoperability.
- Identity, access, policy, governance, compliance, secure configuration, and sensitive information handling.
- Scalable deployment patterns, monitoring, observability, cost, performance, resilience, and service management considerations.
Workplace, Collaboration, and Knowledge Systems
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- Workplace, collaboration, document management, and knowledge sharing patterns.
- Enterprise information models, integration concepts, information governance, compliance, and records management.
- Knowledge discovery, productivity enablement, information protection, classification, identity, and user access considerations.
Data & Integration
- Data architecture, governance, quality, access, lifecycle, and classification considerations.
- Structured and unstructured data, document processing, knowledge retrieval, indexing, and search patterns.
- Integration, API, enterprise application, and interoperability concepts.
Modern Engineering
- Modern delivery, engineering, automation, orchestration, and operational practices.
- Repeatable environment management, controlled release, change, deployment, and lifecycle practices.
- Secure delivery, testing, validation, assurance, observability, reliability, service transition, and operational readiness.
Security & Governance
- Security architecture, identity security, data protection, privacy, regulatory compliance, and secure-by-design principles.
- AI risk management, governance, auditability, logging, monitoring, and assurance controls.
- Supplier, third-party, customer data isolation, and boundary considerations.
Experience
Desirable experience:
- Designing and delivering enterprise-scale technology, automation, data, AI-enabled, or software-integrated solutions.
- Working in customer-facing architecture, consultancy, or pre-sales roles.
- Translating business requirements into solution designs, roadmaps, operating models, and delivery plans.
- Creating reference architectures, implementation patterns, proposal content, and reusable delivery frameworks.
- Applying modern delivery, secure engineering, automation, release management, or platform operating practices.
- Working with security, governance, compliance, service design, and operational transition teams.
- Shaping commercial opportunities, bids, statements of work, managed service propositions, or customer workshops.
- Defining, packaging, or launching repeatable technology product offerings, accelerators, or customer-facing propositions.
Qualifications & Certifications
Desirable certifications include:
- Architecture, service design, or enterprise architecture certifications
- AI, machine learning, or data-related certifications
- Security, privacy, risk, or governance certifications
- Modern delivery, service management, or operational excellence certifications
- Responsible AI, AI governance, or data protection training
- Relevant industry or professional certifications aligned to the role scope
Equivalent experience may be considered in lieu of certifications.
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