Senior AI Security Engineer (GenAI & LLM Protection)

Peover Superior
6 days ago
Create job alert

Senior AI Security Engineer (GenAI & LLM Protection)

Location: Knutsford, Cheshire (Hybrid: 3 days in office / 2 days remote)
Duration: Long-term contract (Until July 2026)
Rate: £600 per day
IR35 Status: Inside IR35 (Umbrella PAYE)

The Opportunity

Are you ready to define the future of AI Security in the financial sector? We are representing a global technology powerhouse tasked with securing the GenAI and Machine Learning roadmap for a Tier 1 Investment Bank.
This is a rare opportunity for a Security SME to move beyond traditional infrastructure and secure the next generation of AI-driven banking services.

The Role

As a Senior AI Security Engineer, you will be the Subject Matter Expert (SME) responsible for protecting the bank's AI/ML ecosystems. You will bridge the gap between Data Science and Cyber Security, ensuring that Large Language Models (LLMs) and Generative AI deployments are resilient, governed, and secure from emerging threats.
Key Responsibilities:

AI Governance & Guardrails: Drive the implementation of AI security platforms (e.g., Azure AI Security, AWS AI Guardrails, or Google Vertex AI Security).

Threat Modelling & Pentesting: Conduct specialized assessments of AI/ML pipelines, model APIs, and data ingestion layers.

Security Automation: Use Python to develop custom tooling and scripts that automate vulnerability testing for AI-related software.

Model Security Research: Stay at the forefront of AI risks (OWASP for LLMs) and execute Proof of Concepts (PoCs) to evaluate new defensive technologies.

Architecture & Design: Contribute to the design of cloud-native GenAI security services, ensuring they align with global data privacy and DLP standards.

Technical Requirements

We are looking for a security professional who understands both the "Cyber" and "Data Science" sides of the equation.
Core Requirements:

AI Security Platforms: Expert knowledge of at least one industry-leading AI security framework (Azure, AWS, Google, or IBM Watson OpenScale).

Cyber Security Fundamentals: Deep experience with firewalls, SIEM platforms, IDS/IPS, and vulnerability management.

Advanced Scripting: High proficiency in Python for automation and security tooling development.

Cloud Ecosystems: Hands-on experience securing workloads within AWS or Azure.

AI/ML Frameworks: A strong understanding of LangChain, PyTorch, or Scikit-learn is a significant advantage.

Experience: 5+ years in Security Engineering, DevOps, or Infrastructure roles.

Preferred Qualifications

Specialized Certifications: CGLCP (Certified GenAI and LLM Cybersecurity Professional) or AWS Certified AI Practitioner.

Industry Standard Certs: CISSP or equivalent.

Financial Services: Experience working in a highly regulated or global banking environment.

Working Pattern & Culture

This role follows a 60% office-based / 40% remote hybrid model. You will join an elite team in Knutsford, an environment designed for innovation, collaboration, and high-performance engineering

Related Jobs

View all jobs

Senior Scientist - Cyber AI/ML Research

Senior Manager AI & Automation

Senior Production Process Engineer

Senior Production Engineer Systems Integration

Senior NX CAM / NC Programmer / NX Post Builder Specialist

Systems Architect

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

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 Many Robotics Tools Do You Need to Know to Get a Robotics Job?

If you’re pursuing a career in robotics, it can feel like the list of tools you should learn never ends. One job advert asks for ROS, another mentions Gazebo, another wants experience with Python, Linux, C++, RobotStudio, MATLAB/Simulink, perception stacks, control frameworks, real-time OS, vision libraries — and that’s just scratching the surface. With so many frameworks, languages and platforms, it’s no wonder robotics job seekers feel overwhelmed. But here’s the honest truth most recruiters won’t say explicitly: 👉 They don’t hire you because you know every tool — they hire you because you can apply the right tools to solve real robotics problems reliably and explain your reasoning clearly. Tools matter — but only in service of outcomes. So the real question isn’t how many tools you should know, but which tools you should master and why. For most robotics roles, the answer is significantly fewer — and far more focused — than you might assume. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable, confident, and ready to contribute from day one.

What Hiring Managers Look for First in Robotics Job Applications (UK Guide)

Robotics is one of the most dynamic, interdisciplinary fields in technology — blending mechanical systems, embedded software, controls, perception (AI/vision), modelling, simulation and systems integration. Hiring managers in this space are highly selective because robotics teams need people who can solve real-world problems under constraints, work across disciplines, and deliver safe, reliable systems. And here’s the reality: hiring managers do not read every word of your CV. Like in many tech domains, they scan quickly — often forming a judgement in the first 10–20 seconds. In robotics, those first signals are especially important because the work is complex and there’s a wide range of candidate backgrounds. This guide unpacks exactly what hiring managers look for first in robotics applications and how to optimise your CV, portfolio and cover letter so you stand out in the UK market.

The Skills Gap in Robotics Jobs: What Universities Aren’t Teaching

Robotics is no longer confined to science fiction or isolated research labs. Today, robots perform critical tasks across manufacturing, healthcare, logistics, agriculture, defence, hospitality and even education. In the UK, businesses are embracing automation to improve productivity, reduce costs and tackle labour shortages. Yet despite strong interest and a growing number of university programmes in robotics, many employers report a persistent problem: graduates are not job-ready for real-world robotics roles. This is not a question of intelligence or dedication. It is a widening skills gap between what universities teach and what employers actually need in robotics jobs. In this article, we’ll explore that gap in depth — what universities do well, where their programmes often fall short, why the disconnect exists, what employers really want, and how you can bridge the divide to build a thriving career in robotics.