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

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Do you want to be at the heart of some of the biggest and most ambitious AI & Data projects?

We are seeking a lead AI Architect to bridge the gap between innovation and impact within our Security & Justice practice.

This role moves beyond experimentation and Proof-of-Concepts - you will architect and operationalise mission-critical AI systems, working with government and public sector organisations on some of their most high profile priorities – an opportunity to achieve measurable societal impacts using your skills.

Working at the intersection of client needs and engineering excellence, you will ensure that our solutions, spanning GenAI and traditional ML, are technically feasible, cost-efficient, and ready for the rigors of the real world.

If you are a builder who thrives on deploying production-grade AI into complex, high-stakes environments, we want to hear from you.

Connect to your career at Deloitte 

Deloitte drives progress. Using our vast range of expertise, we help our clients' become leaders wherever they choose to compete. To do this, we invest in outstanding people. We build teams of future thinkers, with diverse talents and backgrounds, and empower them all to reach for and achieve more.  

What brings us all together at Deloitte? It’s how we approach the thousands of decisions we make every day. How we behave, our beliefs and our attitudes. In other words: our values. Whatever we do, wherever we are in the world, we  lead the way ,  serve with integrity ,  take care of each other ,  foster inclusion , and  collaborate for measurable impact . These five shared values lead every decision we make and action we take, guiding us to deliver impact how and where it matters most.

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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.