Key Responsibilities:Lead the Exploration of opportunities for the development and deployment of AI based solution across our solution.Lead the identification, evaluation, and delivery of AI opportunities that enhance Army training capability and operational effectiveness.Act as the technical authority and primary point of contact for AI architecture, governance, ethics, and regulatory compliance.Define and own scalable, secure AI/ML architectures aligned with enterprise strategy, Omnia principles, and hybrid cloud environments.Design end-to-end AI solutions, including data pipelines, feature engineering, model development, deployment, and operational monitoring.Provide architectural leadership across the full lifecycle, ensuring alignment with DevSecOps, security, and engineering best practices.Establish standards for model performance monitoring, reliability, risk management, and continuous improvement (MLOps).Collaborate with Enterprise Architecture, engineering leaders, and Army/MoD stakeholders to integrate AI capabilities into existing platforms and services.Evaluate and recommend AI platforms, tools, and frameworks to enable scalable experimentation and production deployment.Lead technical horizon scanning, prototyping, and structured experimentation of emerging AI technologies.Develop AI roadmaps and transition architectures to support the evolution of defence training systems.Ensure AI solutions meet security, resilience, and compliance requirements within regulated defence environments.Who we are looking for:You'll have a mission focus, and the enthusiasm and drive to 'get things done'. You'll want to work in collaboration with other defence training organisations, and the British Army. You won't let bureaucracy get in the way of what needs to be done, you'll learn lessons and share these lessons across the team. You'll understand what it means to put the mission first.Essential Skills and Experience:Proven experience architecting AI solutions in secure or classified environments, with strong knowledge of data governance and access control.Proven experience designing and delivering enterprise-scale AI/ML solutions from concept through to production.Strong expertise in AI/ML architecture, including model development, deployment, monitoring, and lifecycle management (MLOps).Experience designing scalable, secure solutions within cloud and hybrid environments (e.g. Azure, AWS, or equivalent).Solid understanding of data architecture, including data pipelines, feature engineering, data governance, and model training strategies.Experience integrating AI capabilities into complex legacy and enterprise systems.Demonstrated application of DevSecOps principles, including CI/CD for AI/ML workloads and automated deployment pipelines.Strong knowledge of AI ethics, responsible AI, and regulatory compliance, particularly within regulated or sensitive environments.Experience selecting and evaluating AI platforms, frameworks, and tooling (e.g. Python, TensorFlow, PyTorch, MLflow, Kubeflow, etc.).Ability to define and implement model performance monitoring, drift detection, and continuous improvement approaches.Experience working within secure or regulated environments (e.g. defence, government, healthcare, finance).Strong stakeholder engagement skills, with the ability to communicate complex technical concepts to senior technical and non-technical audiences.Experience working within or alongside enterprise architecture frameworks and governance processes.Demonstrated ability to lead technical design decisions and provide architectural oversight across multiple delivery teams.Desirable Skills and Experience:MSc in Computing, AI/ML, or equivalent professional accreditation (e.g., CEng).Experience working with UK MoD, Defence Digital, or government programmes.Familiarity with secure-by-design and classified environments.Knowledge of simulation, training systems, or digital learning environments.Relevant certifications (e.g. Azure/AWS Architect, TOGAF, AI/ML specialisations).Experience in cloud platforms and container orchestration tools, particularly Azure, AWS, and Red Hat OpenShift.Experience with Hugging Face Transformers to enable capabilities such as intelligent document processing, conversational AI, and semantic search.Knowledge and experience of some of the following tools;Apache Spark, Databricks, Pandas for data processing and feature engineeringSQL, MongoDB for structured and unstructured data managementSageMaker, Azure ML for model deployment, tracking, and monitoring.Terraform, Ansible, GitLab CI/CD, Jenkins for infrastructure automation and DevSecOpsJBRP1_UKTJ