10 of 10 Permanent High Availability Jobs in West London

environment stability and release governance, and partner closely with Salesforce, MuleSoft, QA, and Operations. You will be responsible for ensuring seamless software delivery through high-quality automation and robust deployment strategies. *CANDIDATES MUST BE ELIGIBLE FOR SC CLEARANCE* What you'll be doing: CI/CD Pipeline Management: Design … Ansible to automate infrastructure provisioning and maintain consistency across environments. MuleSoft Integration: Oversee deployment and lifecycle management for MuleSoft APIs and integrations, ensuring high availability and reliability. (CloudHub 2.0, Runtime Manager, properties encryption, secret management via Azure Key Vault). How this aligns to Capita's values: Customer ...

prospective customers (new logos). Engage with stakeholders from C-level executives to technical engineering teams , translating complex technical and business requirements into high-level and detailed technical architectures. Provide expert consultancy on network strategy, transformation roadmaps, best practices, and emerging technologies. Build strong, long-term relationships that support … security technologies. Ensure all designs are scalable, resilient, secure, and aligned to customer requirements, technical strategy, and industry best practices Lead creation of high-quality architectural documentation, including HLA, HLD, LLD, standards, and design patterns. Ensure designs align with industry best practices, compliance requirements, and customer governance processes. ...

driven architecture, and RTF networking. Collaborate with security teams to ensure policy enforcement, encryption, identity, and SC-compliant design. Oversee NFRs including performance, scaling, high availability, disaster recovery, and observability. Guide developers and DevOps engineers through build, test, deployment, and operational handover. Ensure alignment with enterprise platforms including … effort estimation, planning, stakeholder engagement, and technical risk management. Contribute to the evolution of C4EE playbooks, templates, governance artefacts, and reusable assets. Key Deliverables High-level and low-level design documents API specifications and sequence diagrams Architecture Decision Records (ADRs) Reusable patterns, templates, and standards Architecture review outputs ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...

feature engineering and model training utilising both structured and unstructured large-scale datasets. Production Deployment: Deploy and supervise ML models in production environments, ensuring high availability, optimal performance, and continued relevance. Experimentation: Design and analysis of A/B tests and offline experiments to evaluate model efficacy … . . Experience using ML Training frameworks (e.g., TFX, Kubeflow Pipelines SDK) and Model Serving technologies (eg. Tensorflow Serving, Triton, TorchServe). Experience with high-volume data processing and real-time streaming architectures. Strong understanding of recommendation system design and personalisation algorithms. Familiarity with Generative AI and its applications ...