Senior Cloud & AI Security Enablement Engineer

RELX

Greater London

Hybrid

GBP 90,000 - 150,000

Full time

5 days ago

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Benefits offered by this job

Generous holiday allowance

Life assurance

Pension scheme

Share option scheme

Job summary

LexisNexis Intellectual Property Solutions (LNIP) in the United Kingdom seeks a Senior Cloud & AI Security Enablement Engineer to design and implement reusable cloud guardrails, enable secure AI adoption, and partner with Security Architecture, BISO, AppSec, and DevOps teams.

You will translate security standards into practical patterns, develop AI-assisted workflows, and measure impact on delivery speed and risk.

Qualifications

Experience in machine learning engineering and cloud security.
Hands-on with major cloud platforms (AWS or Azure).
IAM, logging, encryption, secrets management, network and storage security.
IaC and cloud security tooling such as CSPM, CNAPP, IaC scanning, SAST, SCA, secrets scanning, or policy-as-code.
Able to translate standards and architecture requirements into practical implementations.

Responsibilities

Design 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.
Establish cloud security metrics and dashboards covering control adoption, misconfiguration trends, remediation progress, recurring issues, and exception patterns.

Skills

Cloud security

Security architecture

DevSecOps

AI security

IAM

Automation

Tools

CSPM

CNAPP

IaC scanning

SAST

SCA

Policy-as-code

Job description

Enthusiastic about securing cloud platforms and AI-driven solutions in a rapidly evolving technology landscape? Do you enjoy designing security frameworks, enabling secure development practices, and partnering with engineering teams to build resilient, scalable, and compliant cloud and AI environments.

About The Team

LexisNexis Intellectual Property Solutions (LNIP) is the global leader in patent intelligence, bringing clarity to innovation for businesses, law firms, universities, and government agencies worldwide. Our mission is to help the innovation community make better decisions faster, with greater confidence, combining the world’s most trusted patent data with sophisticated analytics, AI-powered insights, and purpose-built workflows.

Protégé in PatentSight is LNIP’s next-generation agentic AI assistant, purpose-built for strategic patent analysis. Protégé replaces complex filter-based workflows with natural language questions, surfacing structured, decision-ready insights grounded in trusted data and established metrics.

About The Role

We are seeking a Senior Cloud & AI Security Enablement Engineer to provide hands‑on cloud security engineering capability while also enabling the secure and effective use of AI across Information Security and product engineering teams.

This role sits between Security Architecture, BISO/Application Security, Cloud/Platform Engineering, and GRC. It is designed to reduce manual workload on Security Architecture and BISO/Application Security by turning recurring security requirements into reusable cloud guardrails, automated controls, AI‑assisted workflows, secure design patterns, and developer‑facing enablement materials.

The successful candidate will be a practical builder and security partner who can operate cloud security controls, support secure AI adoption, and use AI to improve the speed and consistency of security delivery.

Key Responsibilities

Cloud Security Engineering

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.

AI Security Enablement

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.

Secure AI Application and Cloud‑AI Guardrails

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 in partnership with Sec Architecture & AppSec.
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 in partnership with BISO/AppSec.

Security Enablement and Workload Reduction

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.

Requirements

Essential -

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.

Desirable-

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.

Why Join Us?

Join our team and contribute to a culture of innovation, collaboration, and excellence.

Work in a way that works for you

We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long‑term goals.