AI Deployment Engineer

O Partners · London Area, United Kingdom
LinkedIn

Posted

Aug 17, 2026 (12d ago)

Seniority

Not Specified

Work Model

Not Specified

Type

Not Specified

Category

Data & ML

Salary

£130k+ ≈ $165,100 USD

Skills

Airflow AWS Azure Docker GCP Kubernetes OAuth Python

Description

AI Deployment Engineer - London - £130,000 We’re working with an innovative fintech business that is building out its internal AI capability and is looking for an AI Deployment Engineer to join the team. This is a highly technical role focused on taking AI prototypes and turning them into reliable, production-ready solutions. You’ll own the infrastructure underneath AI deployments, including data pipelines, system integrations, APIs and the tooling required to ensure AI solutions operate reliably and at scale. You’ll work closely with AI specialists to take concepts from prototype through to production, connecting AI solutions into the wider business ecosystem and building the technical foundations for future AI deployments. Key Responsibilities Build and maintain production-grade data pipelines, storage and data infrastructure supporting AI deployments. Integrate AI solutions with CRMs, ERPs, SaaS platforms and internal business systems. Develop and maintain APIs, webhooks and middleware enabling AI agents to interact with business systems. Take AI prototypes into production by hardening, scaling and improving reliability. Build monitoring, logging and alerting across AI pipelines and infrastructure. Manage data models, schemas and storage. Troubleshoot integration issues, data inconsistencies and production problems. Help establish the technical foundations for the organisation’s growing AI capability. What We’re Looking For 3–5 years’ experience in software or data engineering, with strong exposure to integrations, data pipelines and production infrastructure. Strong Python skills, with experience building production-grade pipelines from scratch. Good understanding of REST APIs, webhooks, OAuth and event-driven architectures. Experience with orchestration tools such as Airflow, Prefect or Dagster. Experience working across AWS, Azure or GCP. Strong experience with Docker and Kubernetes. Experience integrating disparate business systems, SaaS platforms, databases and third-party APIs. Comfortable working with Microsoft 365 and Microsoft Copilot.