Data & AI Engineering Lead

We’re looking for a Data & AI Engineering Lead to help build and scale our data engineering and AI capabilities. This role sits at the intersection of data pipelines, machine learning operations, and applied AI—particularly focused on turning concepts into scalable, secure, and high-impact solutions.

You’ll play a key role in building robust infrastructure for data and AI product delivery, while also contributing directly to solution development across areas like MLOps, LLMs, and AI agents. This is a growth role: ideal for someone who wants to help shape the practice while growing into technical leadership.

The position and team is part of the Global Data & Analytics department. TCC’s D&A function adds value by enabling different business units and geographies with insights and analytics solutions, in designing and executing high-quality loyalty campaigns for our retail clients.

Role and Responsibilities

Vision & Leadership

• Define and refine the roadmap for data platform optimisation, model development and deployment, and intelligent automation – in line with the broader Global Data, Analytics & AI strategies

• Contribute to the setup of MLOps standards, reusable architecture components, and best practices in the data and AI engineering domain

• Manage partners and vendors in data & AI solution delivery projects

• Stay up to date with evolving best practices in generative AI, model serving, and retrieval-augmented generation (RAG)

Engineering & Delivery

• Design and develop data pipelines, machine learning models, and train/test/deploy workflows using Azure-native tools (CI/CD)

• Operationalise ML and AI use cases by building infrastructure, APIs, and serving solutions

• Build and deploy production-grade Large Language Models (LLMs) and AI agent solutions

• Ensure high availability, scalability, and resilience of AI and data systems, working closely with IT, security, and infrastructure teams

• Support exploration and integration of ML and AI use cases, identifying business-relevant opportunities for next-generation intelligent systems

People & Capability Development

• Build and lead a high-performing, multidisciplinary team focused on data engineering, MLOps, and AI solution development

• Coach and mentor team members

• Lead hiring, onboarding, and capability building across geographies

• Foster a culture of continuous learning, innovation, and knowledge sharing

Key Relationships

External:

Clients (data, marketing, IT, CRM), vendors / partners

Internal:

D&A, IT, Commercial, Delivery / Ops, Risk, Omnichannel, (Extended) Leadership Team

Knowledge, Skills & Experience

• 4–7 years of experience in roles combining data engineering, MLOps, and applied ML

• Proven experience setting up, developing and maintaining cloud- and API-based data platform infrastructure

• Hands-on experience in designing and building data pipelines (Azure Data Factory)

• Strong command of Python, Git, cloud-based ML tooling (Azure ML and MLflow), CI/CD pipelines and containerised environments (e.g., Docker, Azure DevOps repositories)

• Demonstrated talent for technical leadership, with the ability to dive into architecture, code, statistical methodology, and delivery when needed

• Deep understanding of software engineering principles, data architecture, data governance, and platform security

• Excellent stakeholder management skills, with the ability to translate between business, data science, and engineering

• Proficient in project management, using tools like Azure DevOps

Strong Preference:

• Practical experience working with LLMs (e.g., OpenAI, Azure OpenAI, Hugging Face) and AI agents, including their deployment and integration into enterprise systems

• Exposure to RAG, vector databases, and orchestration tools like LangChain or Semantic Kernel

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field

Bonus:

• Experience in retail industry or data-sensitive domains (data contracts / DPAs)

• Worked in multidisciplinary teams (data science, product, engineering) in a complex organisation

• Knowledge of data governance, responsible AI practices, and regulatory requirements

Core Values

At the heart of our business are our core values, which we regard as fundamental to our way of working. We believe in:

Respect: Treat others the way they would like to be treated

Truth: Honest and open at all times, learning from successes & mistakes

Collaborate: Making us smarter and better as one team

Care: About TCC, our future, our colleagues, our clients, our community