Forward Deployed AI Engineer

Position Overview

As a Forward Deployed AI Engineer within Convatec’s AI Centre of Excellence, you will help turn AI opportunities into practical, production-ready solutions that improve how our business works. Embedded directly with business teams, you will take ownership of AI-enabled workflows from discovery and design through to build, testing, deployment, monitoring and handover.

This is a senior, hands‑on delivery role for someone who can work independently across architecture, integration, Dev Ops, MLOps, AIOps, data engineering and governance. You will make technical decisions, solve complex integration challenges and ensure AI solutions are reliable, scalable and safe to run in live business environments. You will work with technologies such as Microsoft Copilot Studio, Microsoft Fabric, Azure Dev Ops, Azure AI Foundry and SAP Joule, helping to design, build and deploy AI workflows that connect into real operational processes across Convatec.

We are looking for someone who is comfortable being the senior technical voice on an initiative: setting standards, guiding others, managing technical risks and ensuring solutions are successfully handed over to the teams who will use and support them.

• Workflow implementation and engineering:
  Design, build and deploy AI-enabled workflows using Azure AI Foundry, Microsoft Copilot Studio, Microsoft Fabric and SAP Joule. This includes agent orchestration, automation triggers, prompt integration, exception handling and reusable delivery patterns.
• API integration and service connectivity:
  Connect AI workflows to enterprise systems through REST APIs, event streams and Microsoft Fabric data pipelines, ensuring secure and reliable data flows across platforms such as SAP, Salesforce and Convatec’s data lake.
• Technical leadership and standards:
  Act as the senior technical voice within embedded initiatives, setting engineering standards, guiding Applied AI Engineers and business teams, and making sound architecture and integration decisions within agreed guardrails.
• Testing, validation and quality assurance:
  Plan and execute functional, regression and edge‑case testing, including failure scenarios, fallback paths, escalation triggers and data quality checks. Assess when workflows are ready for production release.
• Rapid prototyping and feasibility assessment:
  Build time‑boxed prototypes in the AI Landing Zone sandbox to test technical options, demonstrate value and provide clear go/no‑go recommendations before full delivery.
• Dev Ops, MLOps and AIOps ownership:
  Implement and maintain CI/CD, monitor and release pipelines using Azure Dev Ops, including version control, rollback capability, automated testing and production health monitoring.
• Documentation and operational handover:
  Produce clear technical documentation and handover materials so business and operational teams can maintain, monitor and extend AI workflows after delivery.
• Decision‑making authority:
  Make technical recommendations on workflow architecture, integration patterns, tooling choices, production readiness and handover quality within the scope of each initiative.

• Minimum 4+ years’ experience in software engineering, AI/ML workflow delivery or systems integration, with a demonstrable track record of full‑stack delivery in production AI environments.
• Minimum 2+ years’ operating in a senior or lead technical capacity, making independent architectural decisions and guiding other engineers without formal line management authority.
• Hands‑on, production‑grade experience across Dev Ops, MLOps and AIOps — CI/CD pipeline ownership, model versioning, automated testing and live system monitoring — not as adjacent knowledge but as daily practice.
• Strong proficiency in Python and SQL; solid REST and event‑driven API integration experience, including enterprise systems such as SAP (via SAP Joule) or Salesforce.
• Hands‑on experience with Azure AI Foundry, Microsoft Copilot Studio and Microsoft Fabric in production delivery contexts.
• Demonstrated ability to self‑direct across the full delivery lifecycle — from discovery and design through to production deployment and…