AI Engineer
| Company: | Aventum Group |
|---|---|
| Salary: | Not specified |
| Hours: | Full-time |
| Location: | London, EC3R 8AF |
| Working pattern: | Hybrid - 2 days remote |
| Job type: | Permanent |
| Posting date: | 22 Jul 2026 |
| Closing date: | 21 Aug 2026 |
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.
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