Integration Engineer (Software Engineering + AI Product Integration)

Role Summary

As an AI Engineer, you will design, implement, and productionise behavioural learning systems that integrate directly into our digital products and workflows.

Your focus will be on turning advanced behavioural, sequential, and causal AI models into reliable, scalable, and maintainable production systems that power Digital Twins, agentic decision engines, and intelligent automation across our digital suite.

This role bridges model development and real‑world implementation.

You will work hands‑on with software engineers, ML engineers, and product teams to ensure behavioural intelligence is embedded end‑to‑end from data pipelines and inference services through to live product decisioning and monitoring.

Key Responsibilities

• Design, implement, and deploy AI models that predict, optimise, or automate decision‑making within production digital workflows.
• Translate behavioural and sequential modelling approaches (e. g. sequence prediction, intent modelling, imitation learning) into robust, production‑ready systems.
• Build and maintain end‑to‑end AI pipelines, including data ingestion, feature engineering, model training, inference, and monitoring.
• Apply causal inference techniques to evaluate the real‑world impact of AI‑driven decisions and support data‑informed product changes.
• Integrate AI services into existing platforms via APIs, microservices, and event‑driven architectures in close collaboration with engineering teams.
• Partner with product and platform teams to ensure AI outputs are actionable, explainable, and aligned with business workflows.
• Support Digital Twin and agentic systems by implementing behavioural dynamics, state modelling, and decision‑process representations.
• Validate deployed models using offline replay, A/B testing, shadow deployments, and simulation frameworks.
• Ensure solutions meet production standards for scalability, reliability, security, and observability.
• Contribute to engineering best practices around testing, versioning, CI/CD, and model lifecycle management.
• Role Requirements
• Strong foundation in machine learning, applied statistics, or software engineering with a focus on building production systems.
• Demonstrated experience implementing sequence or decision‑based models (e. g. LSTM, Transformers, Markov models, RL‑inspired methods) in real applications.
• Practical experience deploying AI models into production environments, not just experimentation or notebooks.
• Familiarity with behavioural modelling concepts such as imitation learning, behavioural cloning, or decision‑process modelling.
• Working experience with causal inference tooling or methodologies (e. g. Do Why, Econ ML, Causal ML) applied to real data.
• Strong Python skills and experience with ML frameworks such as Py Torch or Tensor Flow.
• Experience building or integrating AI systems using APIs, services, pipelines, or orchestration frameworks.
• Comfortable collaborating in engineering‑led product environments, balancing research insight with delivery constraints.
• Strong problem‑solving skills, with the ability to iterate quickly from prototype to production.
• Management Duties
• No

We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work.

We aim to hire our industry's finest people because the best people drive the best outcomes.

And we forever challenge the status quo because we know there are always ways to improve things.

Because together, we're limitless.

We value applicants from all backgrounds and foster a culture of inclusivity.

We understand the need for flexibility, so work in a hybrid model.

Please let us know if you require any reasonable adjustments during the recruitment process.

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