About the role
2. Key ResponsibilitiesEngineering Strategy and Function Ownership
- Define the technical strategy for the engineering function across AI, data and analytics, and product engineering.
- Establish the technology roadmap, platform standards and architectural principles adopted across the organisation.
- Build and maintain the engineering capability plan, including skill development, team structure and workforce planning.
- Manage relationships with technology partners and platform vendors and evaluate new tooling and platforms for adoption.
- Own engineering budgets, tooling investment and infrastructure cost governance.
- Hold accountability for technical delivery across all client engagements and internal product development.
- Establish delivery methodology, engineering standards, quality gates and release governance.
- Oversee resource allocation, utilisation and capacity planning across concurrent engagements.
- Monitor delivery health, manage technical and schedule risk, and own resolution of technical escalations.
- Report engineering performance, risk and capacity to the Director.
- Own solution architecture and data architecture standards across the organisation.
- Approve the technical approach, feasibility position and effort estimation for new engagements.
- Contribute directly to architecture design for complex, high-risk or strategically significant engagements.
- Review and approve the technical content of client proposals and statements of work.
- Define and enforce code review, design review and security review standards.
- Maintain final approval authority on architecture decisions and technical debt trade-offs.
- Drive the development of reusable frameworks, accelerators and reference implementations.
- Lead, structure and scale the engineering organisation across all technical disciplines.
- Own technical hiring, including role definition, assessment frameworks and interview panels.
- Establish performance management, career progression paths and technical competency frameworks.
- Mentor senior engineers and develop the next layer of technical leadership.
- Ensure all solutions are deployed securely with appropriate access controls, data privacy measures and regulatory compliance.
- Define governance for AI systems including model evaluation, guardrails, monitoring, failure handling and cost control.
- Own production readiness criteria and post-deployment support standards.
- Represent the engineering function in client engagements requiring senior technical authority, including solution workshops, architecture presentations and escalation management.
- Support pre-sales through technical validation, solution design and credibility in client-facing technical discussions.
3. Skills and ExperienceCandidates are assessed on demonstrated capability and delivery track record rather than years of experience. Depth in every discipline is not expected.
- Proven ownership of engineering delivery for a team or function, with accountability for architecture and quality outcomes.
- Track record of taking production systems from requirement through to live deployment and ongoing support.
- Strong, demonstrable expertise in at least one of the following areas: artificial intelligence, data and analytics, or product engineering.
- Applied experience in artificial intelligence covering machine learning, computer vision or natural language processing, and generative AI including large language model applications and retrieval augmented generation.
- Applied experience in data and analytics covering data engineering, pipeline design, warehouse or lakehouse architecture, data modelling, governance, and business intelligence.
- Applied experience in product engineering covering application and product engineering, API and backend service design, enterprise system integration, cloud infrastructure and DevOps.
- Sufficient working knowledge of the disciplines outside the primary area of expertise to conduct architecture review, validate estimates, assess technical risk and assess technical candidates.
- Working knowledge of cloud deployment practice, CI/CD, security, access control and data privacy.
- Understanding of AI governance including model evaluation, guardrails, monitoring and cost control.
- Experience building, structuring and developing engineering teams.
- Delivery experience in a consulting, professional services or client-facing product environment.
- Ability to present and defend technical positions to executive, client and engineering audiences.
- Experience in offshore delivery models serving international clients is desirable.
- Commercial understanding of utilisation, margin and estimation discipline in a services business is desirable.
- Bachelor's degree in Computer Science, Engineering, Information Technology or a related discipline. Postgraduate qualification desirable.