Associate Director, Lead AI Architect, Security & Justice, Engineering, AI & Data, Technology & Transformation

Connect to your opportunity

This is a hands-on role that blends technical expertise, project delivery, and team leadership. Your key responsibilities will include:

Technical Architecture & Engineering

• 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.

Connect to your skills and professional experience

We are looking for candidates who are able to demonstrate skills and experience in some of the following:

Education & experience

• 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.