Consultant - AI Engineer

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Team Overview The Data Science Innovation team is the R&D engine of the Data Science organisation — a team dedicated to exploring, prototyping, and engineering the next generation of AI-powered intelligent systems. We are a team that invents, experiments, and builds things that do not yet exist within the organisation. Our work spans a wide spectrum of problem domains — from intelligent search and autonomous planning systems to knowledge engineering, decision support, and agentic automation — reflecting the deliberate breadth of challenges we take on. The team operates at the intersection of classical AI research, applied engineering, machine learning, and enterprise platform delivery.

This is a hybrid position and involves regular performance of job responsibilities virtually as well as in-person at an assigned TU office location for a minimum of two days a week. Role Overview And Core Responsibilities · Design and build AI-powered intelligent systems: applying classical AI concepts — search, planning, ranking, retrieval, reasoning, constraint satisfaction, and knowledge representation — to solve real enterprise business problems. · Develop scalable backend services, APIs, data pipelines, and orchestration layers: for AI-driven applications that are production-ready from day one. · Build and maintain search, ranking, recommendation, and knowledge management systems: that support intelligent automation and enterprise knowledge use cases. · Integrate AI capabilities with enterprise applications: including APIs, databases, document repositories, and workflow platforms, ensuring reliability, security, and observability. · Apply understanding of context management, memory systems, and human-in-the-loop patterns: to design AI systems that behave correctly and safely in production environments. · Collaborate with business and technology stakeholders: to understand ambiguous problem areas and translate them into well-scoped, measurable technical solutions. · Evaluate AI techniques, architectures, and tools: based on practical value, reliability, scalability, and business impact — reasoning from first principles rather than relying solely on frameworks or pre-built tools. · Participate in design reviews and code reviews: and contribute to the team's engineering standards and technical culture.