Key Responsibilities:

• Lead the design and delivery of end-to-end AI/ML solutions aligned to organisational strategy and operational objectives.
• Define scalable, secure, and maintainable AI architectures, covering data pipelines, model lifecycle management, deployment, and monitoring.
• Establish architectural standards, design patterns, and engineering best practices.
• Provide technical oversight across the full AI lifecycle: concept, prototyping, production deployment, and optimisation.
• Review and approve solution designs produced by AI Solution Engineers and wider delivery teams.
• Lead, mentor, and develop AI Solution Engineer(s) and associated technical staff.
• Set technical direction and ensure consistency in engineering standards across AI initiatives.
• Conduct code and design reviews to ensure quality, security, and maintainability.
• Identify capability gaps and drive upskilling across the AI team.
• Foster a collaborative, high-performance engineering culture.
• Own technical delivery of AI solutions, ensuring alignment with scope, timelines, and quality expectations.
• Oversee deployment of AI systems into secure or controlled environments.
• Ensure compatibility of AI solutions with DevSecOps pipelines and enterprise technology stacks.
• Define and monitor performance metrics for deployed models, ensuring operational effectiveness and reliability.
• Lead remediation and optimisation efforts when performance, compliance, or operational risks arise.
• Embed ethical AI principles, including fairness, explainability, auditability, and transparency, into solution design.
• Ensure compliance with data governance, security, and regulatory requirements.
• Support accreditation and assurance activities within regulated or defence environments.
• Identify, assess, and mitigate technical risks associated with AI deployment.
• Translate operational or business requirements into AI-enabled solution approaches.
• Engage senior stakeholders, providing clear technical briefings and strategic recommendations.
• Contribute to AI capability roadmaps and innovation strategies.
• Lead technical input into bids, proposals, or programme planning activities where required.
• Conduct horizon scanning to evaluate emerging AI technologies and assess relevance to operational use cases.
• Drive continuous improvement of AI engineering practices, tooling, and delivery methods.
• Champion adoption of MLOps practices to enhance repeatability, traceability, and scalability.
• Promote knowledge sharing and best practice dissemination across engineering teams.

Who we are looking for:

The successful candidate will be a technically astute AI Solution Lead who thrives on engineering execution to realise enterprise goals.You will be responsible for designing and sustaining AI/ML solutions that align with the Army Collective Training Service (ACTS) architectural vision. The AI Solution Lead must possess a deep understanding of AI technologies, with the ability to leverage data insights for model development and optimisation using frameworks such as TensorFlow and PyTorch, while also driving intelligent process automation across the machine learning lifecycle through platforms like Azure Machine Learning. This role is pivotal in aligning AI technology with architecture vision and goals, ensuring secure, interoperable systems to drive and automate data driven decision making. xbpsjku

Essential Skills and Experience:

• Extensive experience in leading technical teams designing and delivering end-to-end AI/ML solutions from concept to production within regulated or controlled environments.
• Strong Python expertise and deep knowledge of ML frameworks
• Experience defining scalable ML architectures, including lifecycle management, monitoring, and resilience strategies
• Experience overseeing deployment using containerisation and orchestration platforms and governing CI/CD and MLOps practices
• Strong understanding of API-led integration within enterprise systems
• Experience embedding responsible AI principles into architectural design
• Strong understanding of secure coding, data protection, and governance frameworks, and experience supporting accreditation or assurance processes
• Accountability for technical risk identification and mitigation
• Experience translating operational requirements into AI-enabled capabilities
• Ability to engage and influence senior stakeholders
• Experience defining AI roadmaps and technical strategy
• Strong decision-making capability balancing innovation, risk, and operational constraints
• Proven experience leading, mentoring, and developing AI/ML engineers
• Strong systems-level analytical thinking
• Ability to operate autonomously with accountability for outcomes
• Degree in relevant discipline or equivalent senior-level experience