Principal AI Engineer

About the Role

The client is seeking a hands-on Principal AI Engineer to provide senior technical leadership within the AI Engineering Team. This role is responsible for shaping the engineering direction and delivery of AI-enabled platforms and solutions developed by the AI Team and, where required, supporting other teams across the directorate.

This is a senior technical lead position focused on practical delivery, strong engineering standards, and production-grade AI implementation within a complex, legacy-constrained environment. It demands a technologist who can articulate the art of the possible while delivering grounded, practical solutions within that environment. The role requires deep technical credibility, a track record of hands-on problem solving under delivery pressure, and the ability to guide engineers through modern AI and data practices while balancing compliance, risk, and operational realities.

The Principal AI Engineer will contribute technical expertise to strategic discussions and provide advice to senior stakeholders, while remaining primarily accountable for engineering outcomes, mentoring the team, and ensuring solutions are scalable, secure and aligned with the Department’s AI Assurance and governance requirements.

Key Responsibilities

Hands-On Technical Leadership and Delivery

• Lead the design and implementation of scalable, secure AI solutions aligned with the Department’s AI Assurance Framework.
• Define technical patterns, reusable components, and engineering standards that accelerate AI delivery within the AI Team.
• Provide hands-on guidance across data engineering, model integration, MLOps pipelines, and legacy system integration.
• Lead delivery of Generative AI use cases including RAG pipelines, vector search, embeddings, prompt engineering, and agentic workflows.
• Embed DevOps and MLOps practices, including testing, monitoring, performance optimisation, and lifecycle management of AI systems.
• Ensure solutions are production-grade, supportable, and compliant with privacy, security, and responsible AI requirements.

Collaboration & Stakeholder Engagement

• Contribute technical insight to AI architecture and strategy discussions in partnership with senior stakeholders.
• Provide clear advice on feasibility, trade-offs, risks, and delivery sequencing.
• Translate complex technical trade-offs into clear language for decision-makers.
• Understand and manage AI risks, compliance obligations, and life cycle management in a regulated environment, ensuring all solutions align with the client’s requirements for data protection, AI ethics, and privacy-by-design principles.
• Ability to work across directorates, building collaborative frameworks and shared processes.
• Excellent Stakeholder management, communication and leadership skills, with a proven ability to collaborate effectively across diverse teams and navigate differing perspectives.
• Work hand-in-hand with educators, compliance officers, ICT, and business leaders to ensure AI initiatives are safe, responsible, and add tangible value.
• Ensure diverse voices are heard and actively used to shape products and solutions.
• A practical, responsible approach with a focus on delivery and alignment, not just vision.

 Delivery in Constrained Environments

• Deliver solutions that work within legacy and hybrid technology stacks, prioritising incremental wins that unlock long-term transformation.
• Navigate governance and approval processes while maintaining delivery momentum.
• Translate big-picture AI opportunities into achievable short-term deliverables, balancing ambition with resourcing and compliance realities.
• Work with Product Owners and business teams to sequence delivery for maximum impact with minimal risk.
• Ensure solutions are aligned with the client’s data protection, privacy, and compliance requirements.

Mentorship and Capability Uplift

• Mentor and uplift engineers, analysts, and business users to adopt modern AI engineering practices.
• Foster strong engineering discipline and technical excellence within the team.
• Embed DevOps/MLOps, testing, and monitoring practices into delivery.
• Establish simple, clear guardrails for responsible AI adoption.

Skills & Experience

Essential

• 10+ years’ experience in software engineering, data engineering, or related technical fields.
• Minimum 5 years delivering AI/ML or data-driven solutions in enterprise environments.
• Demonstrated experience productionising AI solutions, including monitoring, retraining, and lifecycle management.
• Strong hands-on expertise in:
  • Python and Agentic AI frameworks.
  • Cloud-native architectures (Azure preferred)
  • Data platforms: Data lakes, APIs, integration patterns
  • AIOps: productionising AI workflows, security, monitoring, retraining
  • Responsible AI practices and compliance alignment
• Proven ability to lead technically while remaining hands-on.
• Track record of mentoring engineers and uplifting technical capability.
• Ability to balance innovation with pragmatism under tight timelines.

Desirable

• Familiarity with education technology and regulated environments.
• Experience with Generative AI use cases including RAG, vector databases, and embeddings.

The Ideal Candidate

You are a technologist first and foremost. You are comfortable architecting solutions, reviewing code, and guiding production implementations when required. You bring credibility with engineers by leading from the front, while also inspiring senior stakeholders with a clear, practical vision of how AI can transform education.

You thrive at the intersection of ambition and pragmatism. You can articulate the art of the possible while remaining grounded in legacy constraints, governance requirements, and real-world delivery pressures. You influence through expertise rather than authority and are motivated by building strong engineering capability and delivering safe, scalable AI solutions.