SC Cleared Principle DevOps Engneer - AI, Azure and AWS
Principle DevOps
Clearance required: BPSS + SC
This role requires additional vetting, which means this could take longer than our normal onboarding process. You will require additional vetting for this position, which means the process can take longer than the usual onboarding process with Capgemini. Please do not hesitate to get in touch if you have any queries.'
Interview process: Tech IV
Location: Remote
Sector: Public
End User: Client
IR35 Status: PAYE via Umbrella company only
Job Description:
Role Summary
Design, build and optimise the enterprise AI Platform that enables the development, deployment and operation of AI products, solutions and services.
The role provides the technical foundations, tooling, automation and governance required to accelerate AI adoption while ensuring security, resilience, scalability and cost-effective delivery across the organisation.
Key Responsibilities
Design, build and maintain enterprise AI Platform capabilities across cloud environments.
Develop and manage AI landing zones, development environments and experimentation platforms.
Implement Infrastructure-as-Code, platform automation and self-service engineering capabilities.
Build and maintain CI/CD and MLOps pipelines supporting AI solution delivery.
Enable secure integration of AI services, APIs, agents, models and orchestration frameworks.
Implement platform observability, monitoring, logging and operational support capabilities.
Establish security controls, governance policies and operational guardrails for AI services.
Support deployment, scaling and operational management of AI products and AI-powered services.
Evaluate emerging AI tooling, foundation model platforms and supporting technologies.
Optimise platform performance, resilience, security and cloud consumption costs.
Develop reusable platform services, accelerators, templates and reference architectures.
Provide technical leadership and guidance to engineering, data and AI delivery teams.
Ensure alignment with enterprise architecture, security, platform and AI governance standards.
Contribute to the development of the organisation's AI capability through a hub-and-spoke operating model.
Required Technical Competencies
Strong platform engineering, cloud infrastructure and software engineering expertise.
Experience building and operating AI platforms in Microsoft Azure and AWS.
Experience with Azure AI Foundry, AWS Bedrock and associated AI ecosystem services.
MLOps, DevOps and CI/CD practices supporting AI solution delivery.
Infrastructure-as-Code technologies such as Terraform, Bicep or CloudFormation.
Containerisation and orchestration technologies including Docker and Kubernetes.
API management, integration architectures and event-driven platforms.
Platform observability, monitoring, logging and operational support tooling.
Identity, security, governance and compliance controls within cloud environments.
Experience supporting AI agents, orchestration frameworks, retrieval architectures and model integration patterns.
Cost optimisation, FinOps and cloud consumption management.
Automation and Scripting technologies supporting platform operations.
Aptitudes/Personal Attributes
Strong technical curiosity and passion for emerging AI technologies.
Strategic mindset with the ability to translate business needs into scalable platform capabilities.
Pragmatic approach to balancing innovation, risk, security and cost.
Passion for automation, standardisation and engineering excellence.
Adaptable and responsive to rapidly evolving AI technologies and market developments.
Collaborative and supportive team player capable of working across multidisciplinary teams.
Strong ownership, accountability and delivery focus.
Excellent communication skills with the ability to explain complex technical concepts clearly.
Comfortable operating in ambiguity and shaping emerging capabilities.
Committed to continuous improvement and knowledge sharing.