Associate Director, Lead AI Architect, Security

• Architect End-to-End AI Systems: Design scalable, secure, and production-ready architectures that integrate LLMs and ML models into complex enterprise workflows.
• Bridge Design & Implementation: Oversee the full technical lifecycle, from selecting the right model architectures to ensuring robust CI/CD and MLOps pipelines.
• Inference & Infrastructure: Deploy and optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.
• Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part of the MLOps lifecycle.
• Drive System Performance: Assess and optimise for performance, cost-efficiency, and reliability, ensuring AI outputs meet the rigorous standards required for our Security & Justice clients.
• System Observability: Design and implement comprehensive evaluation, monitoring, and observability frameworks to track AI performance and system health in real-time.

Delivery & Client Impact

• Pragmatic Problem Solving: Determine where AI adds genuine value and where simpler, traditional engineering approaches are more effective for the client's mission.
• Translate Strategy to Reality: Convert high-level client requirements into detailed technical roadmaps and actionable engineering tasks.
• Mitigate Technical Risk: Proactively identify and manage technical risk, including security vulnerabilities and deployment bottlenecks to drive timely delivery.
• Production Excellence: Shift AI from experimental prototypes to hardened, production-ready services that meet the high security and reliability standards of our clients.

Leadership

• Cross-Functional Team Leadership: Oversee technical teams throughout delivery, ensuring that engineering efforts align with broader project goals and delivery timelines.
• Stakeholder Navigation: Communicate complex technical concepts to non-technical senior stakeholders, building confidence in AI-driven transformations.
• Technical Mentorship: Lead and upskill cross-functional teams of data scientists and engineers, fostering a culture of innovation and engineering excellence.

• Given the pace of change in the space, we do not have a minimum education or certification requirement for this role. Instead, candidates will be expected to demonstrate excellence in the field of AI engineering. This could be from a PhD or equivalent in Computer Science / Machine Learning / Artificial Intelligence, extensive relevant experience in their previous role, personal projects, open source contributions, etc.
• Extensive experience designing, developing, and deploying enterprise-grade AI/ML solutions, including experience managing technical teams and stakeholder relationships is crucial.
• Deep domain expertise in applying AI and Generative AI within a complex industry. Note this does not have to be within security or justice - equally complex areas (such healthcare, government, etc.) with a willingness to upskill in the sector is welcome.
• You can demonstrate experience leading teams to deliver high-quality code that follows software engineering best practices, building with a focus on scalability, reliability, and cost-efficiency.

Technical proficiency

• Demonstrated success leading the end-to-end development and deployment of complex, production-grade AI/ML and Generative AI solutions; evidence of real-world impact highly desirable.
• Expert-level proficiency in Python, and modern AI/ML frameworks, including PyTorch, TensorFlow, and specialised Generative AI libraries (LangChain, LangGraph or related open-source toolkits strongly preferred. Background in Traditional ML/AI is preferred.
• Deep understanding of LLMs, prompt engineering, RAG pipelines, vector databases, and generative architectures; related security practices and evaluation procedures; hands-on experience fine-tuning, deploying and evaluating large-scale production systems.
• Hands-on experience designing and implementing robust evaluation frameworks, security best practices, and ethical guardrails to ensure safe, responsible, and compliant deployment of AI and Generative AI systems.
• Broad experience across major cloud platforms (AWS, Azure, GCP) with Generative AI services. Cloud-agnostic experience is preferred.
• Strong grasp of MLOps/LLMOps principles, including CI/CD for ML, model monitoring, and governance frameworks.
• Proficiency with large-scale data processing and storage technologies (SQL, Spark, Hadoop) is a plus.
• Excellent stakeholder management and communication skills, with proven ability to translate complex AI concepts for diverse audiences.
• You can understand client challenges and propose the best way to solve them. You know when to use AI and when a simpler solution is better.

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