AI Deployment Engineer
Job Description
Our client is a Global FinTech with offices around the world inc Bristol and London in the UK . This AI Deployment Engineer role can be based out of Bristol or London offices. Ideally 3 days per week in the office but could be a little bit more flexible for the ideal candidate.AI Deployment EngineerLocation: Bristol or London - Hybrid, 3 days in the officeSalary: £80,000 - £130,000 p/a dependent on experience + excellent benefitsYou will be the deep technical engine behind their internal AI deployment team.
Where the AI Deployment Strategist scopes and builds agents, you own the infrastructure underneath data pipelines, system integrations, and everything that ensures deployed AI solutions work reliably at scale.You will take prototypes to production, ensure all business systems communicate correctly, and build the technical backbone that our AI tooling depends on.Job ResponsibilitiesOwn the data infrastructure underpinning AI deployments pipelines, storage, and data servingIntegrate AI solutions into the existing business ecosystem: CRMs, ERPs, SaaS tools, and internal systemsBuild and maintain APIs, webhooks, and middleware that allow AI agents to interact with business systemsTake Strategist-built prototypes to production-grade hardening, scaling, and ensuring reliabilitySet up monitoring, logging, and alerting across deployed pipelines and agent infrastructureManage data models, schemas, and storage supporting current and future AI deploymentsTroubleshoot integration failures, data inconsistencies, and production issuesKey SkillsPython & SQL (production-grade pipeline development)REST APIs, webhooks, OAuth, event-driven architectureOrchestration tools: Airflow, Prefect, or DagsterCloud platforms: AWS, GCP, or AzureDocker & Kubernetes Microsoft 365 & Microsoft CopilotDesirable SkillsVector databases and embedding pipelinesReal-time streaming (Kafka, Flink)RPA tooling (UiPath, Power Automate) dbt for data transformationClaude Code, Claude Cowork, or Claude SkillsExperience with vector databases, real-time streaming (Kafka, Flink), or RPA tooling (UiPath, Power Automate).Experience35 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.QualificationsBachelor's degree in Computer Science, Engineering, or equivalent practical experienceTPBN1_UKTJ