About the role
Responsibilities:
- Define the technical vision and architecture for production AI systems that power enterprise data fabric, security, observability, and operational telemetry capabilities.
- Lead the design and evolution of AI platforms and intelligent automation that enable organisations to onboard, transform, monitor, and optimise
- large-scale data workflows with minimal manual intervention.
- Drive end-to-end AI initiatives from problem definition and technical strategy through research, prototyping, production deployment, and continuous improvement across customer-facing environments.
- Architect LLM- and agent-based solutions for complex enterprise use cases including data discovery, schema understanding, transformation generation, anomaly detection, investigation, and natural-language interaction with large-scale datasets.
- Translate ambiguous customer and business problems into scalable AI solutions, partnering closely with Product, Engineering, Design, and customer-facing teams to define strategy, priorities, and measurable outcomes.
- Establish AI engineering and evaluation standards across the organisation, including evaluation methodologies, quality benchmarks, safety and governance controls, observability, and production readiness criteria.
- Drive the integration of AI capabilities into core platform services, ensuring solutions meet enterprise requirements for security, privacy, governance, reliability, and scale.
- Define and champion MLOps and LLMOps practices covering model and agent lifecycle management, evaluation, testing, deployment, observability, cost management, and incident response.
- Lead technical efforts to optimise AI systems for production constraints, including latency, throughput, inference cost, reliability, and multi-tenant scalability.
- Provide technical leadership across multiple engineering teams, influencing architecture and engineering decisions beyond direct reporting lines and establishing reusable patterns and platforms for AI development.
- Mentor and raise the technical bar for engineers and technical leaders in AI system design, distributed systems, software engineering, and production-grade GenAI applications.
- Evaluate emerging AI technologies and research, identify opportunities for meaningful adoption, and establish technical direction where new capabilities can create differentiated customer value.
- Represent AI architecture and technical strategy with senior leadership and customers, communicating complex technical trade-offs and shaping the company's long-term AI roadmap.