Forward Deployed AI Engineer

JOB DESCRIPTION

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, DevOps, 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 DevOps, 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.

Key Responsibilities:

• 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 timeboxed prototypes in the AI Landing Zone sandbox to test technical options, demonstrate value and provide clear go/no-go recommendations before full delivery.
• DevOps, MLOps and AIOps ownership: Implement and maintain CI/CD, monitor and release pipelines using Azure DevOps, 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.

Skills & Experience:

Essential

• 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 DevOps, 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 business handover - without close technical supervision.
• Experience working directly with business stakeholders: translating operational requirements into engineering decisions, managing expectations and owning adoption outcomes.

Desirable

• Experience with SAP Joule or integrating agentic AI solutions with SAP business processes.
• Familiarity with AI Landing Zone design patterns, guardrails and governance frameworks.
• Experience with containerization (Docker/Kubernetes) and cloud-native deployment patterns on Azure.
• Understanding of multi-agent coordination patterns using Copilot Studio or Azure AI Foundry.
• Knowledge of healthcare data privacy requirements including GDPR or HIPAA.

Soft Skills & Attributes

• Self-directed and comfortable working through ambiguity.
• Technically credible, with the confidence to set standards and challenge approaches where needed.
• Outcome-focused, with strong ownership of delivery quality and production reliability.
• Adaptable, able to work across multiple business areas and changing priorities.
• Clear communicator, able to explain technical decisions in a way that business stakeholders can understand and act on.

Education/Qualifications:

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Systems or a related field, or equivalent practical experience demonstrated through a strong delivery track record.

Relevant certifications in Azure (e.g. Azure Developer Associate, Azure Data Engineer Associate, Azure DevOps Engineer Expert) or MLOps/AIOps are desirable.

Travel Requirements:

Position may involve travel up to 10% of the time, mostly within Europe.

Working Conditions:

• Hybrid working model – 1 day per week on-site at our London office

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