AI Deployment Engineer
Emponics
London
Full-time
GBP 80,000 – 130,000
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Our client is a Global FinTech with offices around the world including London and Bristol in the UK .
This AI Deployment Engineer role can be based out of their London offices or Bristol .
Ideally 3 days per week in the office but could be a little bit more flexible for the ideal candidate.
- AI Deployment Engineer
- Location: Bristol/London - Hybrid, 3 days in the office
- Salary: £80,000 - £130,000 p/a dependent on experience + excellent benefits
You will take prototypes to production, ensure all business systems communicate correctly, and build the technical backbone that our AI tooling depends on.
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
- 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
- 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 with vector databases, real-time streaming (Kafka, Flink), or RPA tooling (UiPath, Power Automate).
- 3–5 years in software or data engineering with strong exposure to system integrations, data pipelines, and production infrastructure.
- Strong Python and SQL skills; experienced 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.
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