AI & ML Engineer (Cybersecurity)

Role title: AI the use of AI to counter the rapidly evolving threat landscape is the mission, and we are looking for innovative and passionate people to support this effort. You will bring strong engineering rigour and a security-first mindset to everything you build. Responsibilities: LLM Configuration : Configure, evaluate, and tune large language models for security use cases; including prompt engineering, system prompt design, retrieval-augmented generation (RAG), and tool/function calling. Agent Development: Design, build, and maintain AI agents and agentic workflows using platforms such as Azure AI Foundry, integrating with security toolsets such as SOAR, SIEM, and EDR. Secure Configuration: Implement safeguards to protect against adversarial inputs, including prompt injection and jailbreaks, ensuring AI systems meet robust security standards. System Architecture: Architect AI solutions that are modular, maintainable, and built to scale, adhering to secure development principles. Governance: Implement solutions in line with our AI governance framework, encompassing factors such as data handling, audit logging, human-in-the-loop controls, and regulatory compliance. Cost Management: Monitor and optimise LLM API usage and infrastructure costs, implementing strategies such as caching, model tiering, and guardrails to ensure solutions are economically viable. Collaboration: Work closely with key stakeholders to understand operational pain points, prototype solutions, and iterate based on feedback. Documentation: Produce clear technical documentation to support effective ongoing management of developed capabilities. We are looking for the following skills and experience: 2+ years of experience within AI/ML engineering. Hands-on experience building and deploying AI agents or LLM-powered applications in production environments, and the implementation of appropriate observability and evaluation mechanisms. Practical experience with enterprise AI platforms, such as Azure AI Foundry, AWS Bedrock or Google Vertex AI. Strong understanding of LLM concepts and vector databases: context windows, temperature and sampling parameters, system prompts, tool/function calling. Experience of implementing RAG pipelines end-to-end, including data ingestion, chunking strategies, embedding model selection, and retrieval tuning. Experience of implementing parsing pipelines for tasks such as document processing / ingestion, including handling unstructured data. Demonstrable knowledge of AI security risks (familiarity with OWASP LLM Top 10), and how these can be mitigated. Experience working within enterprise governance frameworks. Proficiency in one or more languages (such as Python), and familiarity with common AI/ML libraries. Ability to build custom tools that can interact with, or be exposed to LLMs. Strong understanding of REST APIs, and the ability to integrate AI solutions with third‑party platforms and internal tooling. Experience of deploying infrastructure-as-code is advantageous. Knowledge of typical enterprise security tooling (such as SOAR, SIEM and EDR) is advantageous. Collaborative and engaging approach to problem solving, and a willingness to work as part of the team. Passionate for diversity, recognising the innovation and competitive edge that comes from a diverse highly skilled team where equal opportunities are truly valued. A problem‑solver, always seeking the best solution for the right outcome. Self‑motivated, results‑focussed, pragmatic with the ability to manage conflicting deadlines and prioritise. #J-18808-Ljbffr