Solution Architect – Cyber Data, Analytics and AI Architect

Cyber UK
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Technical Domain Expert

Senior Solutions Architect

Pre-Sales Engineer / Product Associate

Technical Architect

Databricks Architect

Fabric Architect

Job Title:Solution Architect – Cyber Data, Analytics and AI Architect

Job Type:1-year FTC / Permanent

Job Location:London, UK (Hybrid)

Job Description:

Role: Solution Architect – Cyber Data, Analytics and AI Architect – Data and Digital. As we develop an enterprise-wide data and digital strategy that moves us toward greater focus on the use of data and data-driven insights, we are seeking a Data & Digital Solutions Architect focused on Cyber Data, Analytics and AI Strategy. This role is a hands-on, execution-focused position that helps translate the data and digital strategy to architecture and execution. You will have an understanding of our data and analytical systems at a granular level and function as the organizational data and digital architecture subject matter expert for various projects related to Cyber Program.

The Cyber Data, Analytics and AI function within AXA Group Cyber Center of Expertise (CoE) is responsible for aligning and driving the management of data and analytics related to cyber insurance and services globally. This function is responsible for building a roadmap to create best-in-class cyber risk analytics and AI solutions to ensure connectivity across all AXA entities.

Qualifications, Abilities, and Skills:

  • Moderate to extensive years of experience in relevant architecture roles, with broad and deep expertise in insurance (especially cyber) data strategy and architecture.
  • Working experience with organizations that operate globally across multiple entities is preferred.
  • Understanding of security committees such as CIFIUS.
  • Deep expertise in distributed and decentralized domain design to support multi-entity model architecture.
  • Understanding of data mesh concepts and applicability to support multi-entity architecture.
  • Understanding of federated governance approaches to support multi-entity model.
  • Hands-on experience with architecture and design in:
    • Azure cloud platform environment
    • Big data technologies
    • Machine Learning, AI, LLM knowledge, especially how to apply these concepts to Cyber Data Analytics solutions.
    • Message queues, streaming technologies, and event-driven architecture.
    • Unstructured data management
    • O365 environment
    • Container & orchestration platforms
    • Relational databases and structured query languages
    • Microservices
    • Integration and ETL/ELT technologies
    • Data management and analytics
    • Reference & Master Data Management
    • Access control and security
  • Effective management and leadership skills with the ability to influence departmental strategy.
  • Outstanding organizational skills with attention to detail and ability to handle change.
  • Excellent presentation, communication (oral & written), and relationship-building skills across all levels of management.
  • Excellent problem-solving and analysis skills.
  • Knowledge and active use of Agile, SCRUM, and Continuous Delivery.

Responsibilities:

  • Understand user requirements (desired output & outcome) and existing environments (current state), and translate these into an architecture roadmap.
  • Guide the full lifecycle of a Solution, including:
    • Understanding the Business Capabilities required and translating this to Technical Capabilities and Solution Architecture.
    • Assisting the customer (and business solutions analysts) to define and declare non-functional requirements for building out solution capabilities.
    • Documenting solution interactions and relationships, along with basic infrastructure and application onboarding requirements.
    • Ensuring security by design to meet Information Security policies, Legal, Compliance, Risk, and Regulatory requirements, and industry best practices.
    • Being hands-on to demonstrate tools, patterns, security, performance, scalability, etc., in a lab environment when necessary.
    • Participating in the definition of adoption and experience metrics for the solution to measure its success.
    • Presenting architecture decisions, explaining the end-to-end solution, educating others on how they can contribute, and providing guidance on tool usage.
    • Acting as a strategic advisor to management.
    • Contributing to the development of the Strategic Architecture.
    • Understanding & communicating business problems & technical solutions in appropriate terminology and influencing across business lines.

Apply For Job

#J-18808-Ljbffr

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Write a Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.

Veterans in Machine Learning: A Military‑to‑Civilian Pathway into AI Careers

Introduction Artificial intelligence is no longer relegated to sci‑fi films—it underpins battlefield decision‑support, fraud detection, and even supermarket logistics. The UK Government’s 2025 AI Sector Deal forecasts an additional £200 billion in GDP by 2030, with machine‑learning (ML) engineers cited as the nation’s second most in‑demand tech role (Tech Nation 2024). The Ministry of Defence’s Defence AI Strategy echoes that urgency, earmarking £1.6 billion for FY 2025–28 to embed ML into planning, logistics, and autonomous systems. If you have ever tuned a radar filter, plotted artillery trajectories, or sifted sensor data for actionable intel, you have already worked with statistical modelling—the backbone of machine learning. This guide shows UK veterans how to reframe military experience for ML roles, leverage MoD transition funding, and land high‑impact positions building the models shaping tomorrow’s defence and commercial landscapes. Quick Win: Bookmark our live board for Machine‑Learning Engineer roles to see who’s hiring today.

Rural-Remote Machine Learning Jobs: Finding Balance Beyond the Big Cities

Over the past decade, machine learning (ML) has transformed from a niche research domain into a pervasive technology underpinning everything from recommendation systems and voice assistants to financial forecasting and autonomous vehicles. Historically, the UK’s major tech hubs—particularly London—have been magnets for top ML talent and corporate headquarters. However, remote work has become mainstream, and many ML professionals are realising they can excel in their field while living far beyond the city limits. At MachineLearningJobs.co.uk, we’ve observed a growing interest in positions that allow for a rural lifestyle or a coastal environment, often reflected in search terms like “ML remote countryside” or “tech jobs by the sea.” This surge is no coincidence. Flexible work policies, better rural broadband, and the nature of machine learning tasks—much of which can be done through cloud platforms—are bringing new opportunities to those who wish to swap urban hustle for fresh air and scenic views. Whether you’re a data scientist, ML engineer, researcher, or product manager, a rural or seaside move could reinvigorate your work-life balance. In this article, we’ll unpack why rural-remote ML jobs are on the rise, how you can navigate the challenges of leaving the city, and what you need to do to thrive in a machine learning career beyond the M25. If you’ve dreamt of looking up from your laptop to rolling fields or ocean waves, keep reading—your rural ML role might be closer than you think.