AI Engineer

Role title: AI Engineer

Location:

UK Nationwide (2x a month in the Cheltenham office- hybrid working)
Salary:
Competitive salary and package (Depending on level of experience)

Please Note:

Any offer of employment is subject to satisfactory BPSS and SC security clearance which requires 5 years continuous UK address history (typically including no periods of 30 consecutive days or more spent outside of the UK) at the point of application. The above information relates to a specific client requirement'We are looking for an AI Engineer to develop and optimise AI agents and LLM-powered applications that will help advance Accenture’s Security Operations Centre (SOC) capabilities.

Working closely with security engineers and SOC analysts, you will design, build and ope rationalise AI-powered workflows that integrate with security tooling and help accelerate threat detection and response.

You’ll bring strong AI engineering expertise, with hands-on experience building production-ready LLM applications, agentic workflows and RAG solutions. Previous cyber security experience is advantageous but not essential.

We are at an exciting point in the evolution of defensive cyber operations, with AI playing an increasingly important role in responding to a rapidly evolving threat landscape. We’re looking for innovative, practical engineers who want to apply their AI expertise to this challenge.

ResponsibilitiesLLM 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, following secure development principles.

Governance:
Implement solutions in line with our AI governance framework, covering areas 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, security engineers and SOC analysts to understand operational challenges, prototype solutions and iterate based on feedback.

Documentation:
Produce clear technical documentation to support the effective ongoing management of developed capabilities.

Essential:

We are looking for:2+ years of experience within AI/ML engineering.

Hands-on experience building and deploying AI agents or LLM-powered applications in production environments, including appropriate observability and evaluation mechanisms.

Practical experience with enterprise AI platforms such as Azure AI Foundry, AWS Bedrock, Google Vertex AI or similar.

Strong understanding of LLM concepts and vector databases, including context windows, temperature and sampling parameters, system prompts and tool/function calling.

Experience implementing RAG pipelines end-to-end, including data ingestion, chunking strategies, embedding model selection and retrieval tuning.

Experience implementing parsing pipelines for tasks such as document processing and ingestion, including handling unstructured data.

Knowledge of AI security risks, including familiarity with the OWASP LLM Top 10, and an understanding of how these risks can be mitigated.

Experience working within…