AI Deployment Engineer

AI Deployment Engineer

Our client is a Global FinTech with offices around the world including Bristol and London in the UK. This AI Deployment Engineer role can be based out of the Bristol or London offices and is ideally 3 days per week in the office, though a bit more flexibility may be available for the ideal candidate.

Location: Bristol or London – Hybrid, 3 days in the office.Salary: £80,000 – £130,000 p/a dependent on experience + excellent benefits.

Job Responsibilities

• Own the data infrastructure underpinning AI deployments – pipelines, storage, and data serving
• Integrate AI solutions into the existing business ecosystem: CRMs, ERPs, SaaS tools, and internal systems
• Build and maintain APIs, webhooks, and middleware that allow AI agents to interact with business systems
• Take strategist-built prototypes to production-grade – hardening, scaling and ensuring reliability
• Set up monitoring, logging and alerting across deployed pipelines and agent infrastructure
• Manage data models, schemas and storage supporting current and future AI deployments
• Troubleshoot integration failures, data inconsistencies and production issues

Key Skills

• Python & SQL – production‑grade pipeline development
• REST APIs, webhooks, OAuth, event‑driven architecture
• Orchestration tools: Airflow, Prefect or Dagster
• Cloud platforms: AWS, GCP or Azure
• Docker & Kubernetes
• Microsoft 365 & Microsoft Copilot

Desirable Skills

• Vector databases and embedding pipelines
• Real‑time streaming (Kafka, Flink)
• RPA tooling (UiPath, Power Automate)
• dbt for data transformation
• Claude Code, Claude Cowork or Claude Skills

Experience

• 3–5 years in software or data engineering with strong exposure to system integrations, data pipelines and production infrastructure
• Strong Python and SQL skills; experience building robust, production‑grade data pipelines from scratch
• Deep familiarity with integration patterns: REST APIs, webhooks, OAuth and event‑driven architectures
• Experience with orchestration tools (Airflow, Prefect or Dagster) and transformation frameworks (dbt or similar)
• Comfortable across cloud platforms (AWS, GCP or Azure) and with containerisation (Docker, Kubernetes)
• Experience connecting disparate business systems – SaaS platforms, internal databases and third‑party APIs – and making them work reliably
• Strong debugging instincts and a high bar for reliability and data integrity
• Comfortable with Microsoft 365 and Microsoft Copilot; familiarity with AI productivity tools including Claude Code, Claude Cowork and Claude Skills is a plus

Qualifications

• Bachelor’s degree in Computer Science, Engineering or equivalent practical experience

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