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 teams engineering standards and technical culture.

Required Knowledge And Experiences

Classical AI and Intelligent Systems: Solid understanding of core classical AI concepts: state-space search heuristic search (A* beam search) forward and backward planning partial-order planning constraint satisfaction and optimization. Hands-on experience in one or more applied AI domains search and ranking planning and scheduling recommendation engines rule engines expert systems or knowledge-based systems. Understanding of knowledge representation context management memory systems and intelligent automation patterns.

AI Application Development: Experience building AI applications in areas such as semantic search document intelligence information extraction recommendation systems conversational AI or enterprise decision support. Ability to evaluate AI techniques based on reliability scalability and business impact.

Software Engineering: Strong programming skills in Python; working knowledge of Java or a similar language. Strong understanding of data structures algorithms computational complexity object-oriented design and design patterns. Experience building production-grade APIs microservices data pipelines or event-driven systems. Familiarity with Git CI/CD automated testing logging and monitoring.

Systems and Platform: Good understanding of distributed systems databases concurrency and scalable backend architecture. Awareness of security reliability observability and maintainability requirements for enterprise-scale systems.

Required Technical Skills

Programming: Python (strong); Java or equivalent general-purpose language (working knowledge).

Classical AI and Search: Search algorithms (A* BFS beam search) heuristic planning constraint satisfaction ranking and optimization systems.

AI Systems: Knowledge representation rule-based systems semantic systems planning and scheduling frameworks recommendation and retrieval systems.

Data Engineering and Storage: SQL and relational databases; NoSQL databases; Vector databases Graph databases (e.g. Neo4j Ontop); data pipeline design and ETL/ELT patterns.

Cloud and Infrastructure: Azure AWS or GCP; Docker; Kubernetes; Kafka message queues; cloud-native application design.

Engineering Practices: REST APIs microservices event-driven architectures CI/CD pipelines automated testing logging monitoring and observability tooling.

Preferred Skills (Good to Have)

Experience with Retrieval-Augmented Generation (RAG) vector search embedding-based retrieval

Familiarity on Ontology & Knowledge graph integration.

Familiarity with agentic AI frameworks such as LangChain LangGraph LlamaIndex Semantic Kernel or AutoGen.

Exposure to agentic workflows tool calling multi-agent system patterns or AI evaluation and monitoring.

Understanding of LLM-based agent architectures as a complement to classical AI planning approaches.

Experience with NLP (Natural Language Processing) document intelligence or conversational AI platforms.

TransUnion Overview:

At TransUnion we encourage and are committed to creating a real positive impact and shared sense of purpose within our Workforce for Good which empowers our people to grow innovate and contribute to a better future for our communities and customers. We strive to build an environment where our associates are in the drivers seat of their professional development while having access to help along the way. We recognize that success comes when our associates thrive both professionally and personally; thats why we prioritize work/life flexibility and offer resources for our teams across the globe to collaborate and drive excellence.

Be a part of our Workforce for Good youll work with great people pioneering products and cutting-edge technology.

TransUnion Job Title

Consultant Analytics Consulting