AI Engineer

Accenture
Holywood, UK
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Audit Trail Cyber Security Intrusion Detection and Prevention Python (Programming Language) Machine Learning Open Web Application Security Security Information and Event Management Systems Architecture Systems Integration
+17 more
Unstructured Data Data Processing Data Ingestion Retrieval-Augmented Generation Large Language Models Prompt Engineering Google Vertex AI Generative AI Agentic-AI AI Platforms Scikit Learn Invoking Functions Restful APIs Cyber Warfare Human in the Loop Security Orchestration, Automation & Response Programming Languages

Job description

Working closely with security engineers and SOC analysts, you will design, build and operationalise 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. 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, 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.

Requirements

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 enterprise governance frameworks. Proficiency in one or more programming 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, with the ability to integrate AI solutions with third-party platforms and internal tooling.

Desirable: The following would be beneficial but are not essential: Previous experience within cyber security or security operations. Knowledge of enterprise security tooling such as SOAR, SIEM and EDR. Experience applying AI to security operations, threat detection or incident response. Experience deploying infrastructure-as-code., A collaborative and engaging approach to problem-solving, with a willingness to work as part of a wider team. Self-motivated, results-focused and pragmatic, with the ability to manage competing priorities and deadlines. Someone who values diversity and recognises the innovation and competitive advantage that comes from diverse, highly skilled teams where equal opportunities are genuinely valued.

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Benefits & conditions

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’

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