Senior Cloud & AI Security Enablement Engineer

RELX Group; Exeter; On-site; GBP 45,000 - 85,000; Full time

Benefits offered by this job

Generous holiday allowance

Life assurance

Pension scheme

Employee discounts

Flexible working

Job summary

LexisNexis Intellectual Property Solutions (LNIP) in London’s Farringdon area seeks a senior cloud security engineer to design and embed security patterns across cloud landing zones, CI/CD, and IaC. You will implement guardrails for identity, encryption, logging, and data protection, while aligning with AI-enabled application security and risk management practices.

You will partner with GRC, Legal, and product teams to ensure auditable AI-assisted security workflows and reusable guidance, with

Qualifications

• Experience in machine learning engineering.
• Strong experience in cloud security engineering, application security, security architecture, DevSecOps, or security automation.
• Hands-on experience with at least one major cloud platform such as AWS or Azure.
• Practical knowledge of IAM, logging, encryption, secrets management, network security, storage security, vulnerability management, and secure CI/CD.
• Experience with infrastructure-as-code and cloud security tooling such as CSPM, CNAPP, IaC scanning, SAST, SCA, secrets scanning, or policy-as-code.
• Ability to translate security standards and architecture requirements into practical implementation patterns.
• Familiarity with generative AI, LLM-enabled applications, AI-assisted development, or AI security risks.
• Strong scripting, automation, workflow design, or tooling integration experience.
• Excellent written and verbal communication skills, with the ability to create clear security guidance for engineering teams.
• Experience building cloud security guardrails, landing zone controls, reusable modules, or automated remediation workflows.
• Experience supporting regulated environments or environments with strict customer, contractual, or compliance obligations.
• Familiarity with AI security frameworks and guidance such as the NIST AI Risk Management Framework, NIST Generative AI Profile, OWASP Top 10 for LLM Applications, or Cloud Security Alliance AI security guidance.
• Experience with RAG architecture, AI agents, vector databases, AI APIs, prompt engineering, or AI application threat modelling.
• Experience building security knowledge assistants, prompt libraries, AI-assisted review workflows, or security automation.

Responsibilities

• Build and maintain reusable cloud security guardrails for identity, logging, encryption, network segmentation, secrets management, storage, workloads, containers, serverless services, and data protection.
• Translate security architecture standards into deployable cloud patterns, reference implementations, templates, and engineering-ready requirements.
• Partner with cloud, platform, DevOps, and product engineering teams to embed security controls into cloud landing zones, CI/CD pipelines, infrastructure-as-code workflows, and operational processes.
• Support the implementation and tuning of CSPM, CNAPP, cloud workload protection, IaC scanning, secrets scanning, and related cloud security tooling.
• Develop policy-as-code and control-as-code mechanisms to prevent, detect, and report common cloud misconfigurations.
• Support cloud-native vulnerability and misconfiguration remediation by providing prioritization logic, remediation guidance, and reusable fix patterns.
• Establish cloud security metrics and dashboards covering control adoption, misconfiguration trends, remediation progress, recurring issues, and exception patterns.
• Design and maintain AI-assisted workflows for security intake triage, threat model drafting, architecture review summaries, control mapping, remediation guidance, and risk statement generation.
• Build prompt libraries, review rubrics, validation steps, and human-in-the-loop processes for approved AI use within Information Security.
• Identify repetitive BISO, AppSec, and Security Architecture tasks that can be safely accelerated through AI-assisted processes.
• Partner with GRC, Legal, Privacy, and security leadership to ensure AI-assisted security workflows are auditable, explainable, and aligned with internal risk expectations.
• Measure the effectiveness of AI-assisted workflows, including time saved, consistency improvements, review quality, and reduction in repeat manual work.
• Define secure design patterns for AI-enabled applications, including LLM-based features, retrieval-augmented generation, AI agents, AI APIs, copilots, and automation workflows.
• Establish cloud security requirements for AI workloads, including identity, secrets, data storage, network access, observability, workload isolation, and third-party AI service integrations.
• Support AI application threat modelling and risk reviews in partnership with BISO/AppSec.
• Create developer-facing guidance for safe use of AI coding assistants, AI APIs, data ingestion, vector stores, model outputs, and AI-generated code.
• Convert recurring BISO/AppSec and Security Architecture questions into reusable guidance, decision trees, approved patterns, checklists, and self-service workflows.
• Build lightweight intake and routing mechanisms that help determine when a request needs BISO review, AppSec review, architecture review, GRC input, or engineering remediation.
• Create and maintain a library of approved cloud and AI security patterns that product and engineering teams can reuse.
• Help reduce one-off security consultations by embedding approved security decisions into tooling, templates, and standard delivery workflows.

Skills

Machine learning engineering

Cloud security

Security architecture

DevSecOps

Cloud platforms

IAM

Logging

Encryption

Secrets management

IaC & cloud security tooling

AI security familiarity

Scripting & automation

Security guidance

Tools

AWS

Azure

CSPM

CNAPP

SAST

SCA

Job description

Salary: £45,000 - 85,000 per year

Requirements:

• Experience in machine learning engineering.
• Strong experience in cloud security engineering, application security, security architecture, DevSecOps, or security automation.
• Hands-on experience with at least one major cloud platform such as AWS or Azure.
• Practical knowledge of IAM, logging, encryption, secrets management, network security, storage security, vulnerability management, and secure CI/CD.
• Experience with infrastructure-as-code and cloud security tooling such as CSPM, CNAPP, IaC scanning, SAST, SCA, secrets scanning, or policy-as-code.
• Ability to translate security standards and architecture requirements into practical implementation patterns.
• Familiarity with generative AI, LLM-enabled applications, AI-assisted development, or AI security risks.
• Strong scripting, automation, workflow design, or tooling integration experience.
• Excellent written and verbal communication skills, with the ability to create clear security guidance for engineering teams.
• Experience building cloud security guardrails, landing zone controls, reusable modules, or automated remediation workflows.
• Experience supporting regulated environments or environments with strict customer, contractual, or compliance obligations.
• Familiarity with AI security frameworks and guidance such as the NIST AI Risk Management Framework, NIST Generative AI Profile, OWASP Top 10 for LLM Applications, or Cloud Security Alliance AI security guidance.
• Experience with RAG architecture, AI agents, vector databases, AI APIs, prompt engineering, or AI application threat modelling.
• Experience building security knowledge assistants, prompt libraries, AI-assisted review workflows, or security automation.

Responsibilities:

• Build and maintain reusable cloud security guardrails for identity, logging, encryption, network segmentation, secrets management, storage, workloads, containers, serverless services, and data protection.
• Translate security architecture standards into deployable cloud patterns, reference implementations, templates, and engineering-ready requirements.
• Partner with cloud, platform, DevOps, and product engineering teams to embed security controls into cloud landing zones, CI/CD pipelines, infrastructure-as-code workflows, and operational processes.
• Support the implementation and tuning of CSPM, CNAPP, cloud workload protection, IaC scanning, secrets scanning, and related cloud security tooling.
• Develop policy-as-code and control-as-code mechanisms to prevent, detect, and report common cloud misconfigurations.
• Support cloud-native vulnerability and misconfiguration remediation by providing prioritization logic, remediation guidance, and reusable fix patterns.
• Establish cloud security metrics and dashboards covering control adoption, misconfiguration trends, remediation progress, recurring issues, and exception patterns.
• Design and maintain AI-assisted workflows for security intake triage, threat model drafting, architecture review summaries, control mapping, remediation guidance, and risk statement generation.
• Build prompt libraries, review rubrics, validation steps, and human-in-the-loop processes for approved AI use within Information Security.
• Identify repetitive BISO, AppSec, and Security Architecture tasks that can be safely accelerated through AI-assisted processes.
• Partner with GRC, Legal, Privacy, and security leadership to ensure AI-assisted security workflows are auditable, explainable, and aligned with internal risk expectations.
• Measure the effectiveness of AI-assisted workflows, including time saved, consistency improvements, review quality, and reduction in repeat manual work.
• Define secure design patterns for AI-enabled applications, including LLM-based features, retrieval-augmented generation, AI agents, AI APIs, copilots, and automation workflows.
• Establish cloud security requirements for AI workloads, including identity, secrets, data storage, network access, observability, workload isolation, and third-party AI service integrations.
• Support AI application threat modelling and risk reviews in partnership with BISO/AppSec.
• Create developer-facing guidance for safe use of AI coding assistants, AI APIs, data ingestion, vector stores, model outputs, and AI-generated code.
• Convert recurring BISO/AppSec and Security Architecture questions into reusable guidance, decision trees, approved patterns, checklists, and self-service workflows.
• Build lightweight intake and routing mechanisms that help determine when a request needs BISO review, AppSec review, architecture review, GRC input, or engineering remediation.
• Create and maintain a library of approved cloud and AI security patterns that product and engineering teams can reuse.
• Help reduce one-off security consultations by embedding approved security decisions into tooling, templates, and standard delivery workflows.

Technologies:

• Agentic AI
• AI
• AI Agents
• AWS
• Azure
• CI/CD
• Cloud
• DevSecOps
• DevOps
• IAM
• Support
• LLM
• Machine Learning
• Network
• OWASP
• RAG
• Security
• Serverless

More:

We are LexisNexis Intellectual Property Solutions (LNIP), the global leader in patent intelligence, helping businesses, law firms, universities, and government agencies make better decisions faster with trusted patent data, sophisticated analytics, AI-powered insights, and purpose-built workflows. Protégé in PatentSight is our next-generation agentic AI assistant for strategic patent analysis, designed to replace complex filter-based workflows with natural language questions and structured, decision-ready insights. We offer a culture of innovation, collaboration, and excellence, along with flexible working, generous holiday allowance, the option to buy additional days, life assurance, a contributory pension scheme, share options, optional dental insurance, maternity, paternity and shared parental leave, an employee assistance programme, learning and development resources, employee discounts, wellbeing initiatives, study assistance, sabbaticals, and access to employee resource groups with volunteer time. This is a full-time role based in Farringdon, and we are committed to an accessible hiring process and equal opportunity employment.

last updated 36 week of 2026