AI platform engineer in procurement

We are building an AI-native procurement platform where machine learning models, retrieval systems, AI agents, and intelligent applications operate as production services. This role is responsible for ensuring those capabilities can be deployed, monitored, evaluated, and continuously improved at enterprise scale.

• Design, build, and maintain the platform capabilities required to deploy and operate production AI systems.
• Develop automated pipelines for model packaging, deployment, versioning, testing, rollout, rollback, and lifecycle management.
• Build evaluation frameworks that continuously measure model quality, retrieval effectiveness, latency, cost, and business performance.
• Implement monitoring for models, inference services, retrieval pipelines, and AI agents using metrics, logging, tracing, and operational telemetry.
• Partner with Machine Learning Engineers to productionize models and improve deployment reliability.
• Collaborate with Platform Engineering to optimise runtime environments, infrastructure, scaling, security, and operational resilience.
• Develop deployment strategies supporting experimentation, canary releases, A/B testing, and progressive rollouts.
• Implement governance controls supporting reproducibility, version management, auditability, and operational compliance.
• Automate repetitive operational activities wherever possible, improving engineering productivity and deployment confidence.
• Contribute to the evolution of the Agentic Software Development Lifecycle (ASDLC) by embedding evaluation, automation, and operational intelligence into AI delivery processes.

• Experience operating production machine learning or AI platforms.
• Software engineering skills with experience building automation, deployment pipelines, and operational tooling.
• Exposure to cloud-native infrastructure, container platforms, and distributed systems.
• Deep understanding of CI/CD, infrastructure as code, release automation, and production operations.
• Strong capability implementing observability across complex distributed applications.
• Understanding of model lifecycle management, deployment strategies, experimentation, and production evaluation.
• Track record collaborating across Platform Engineering, Machine Learning, Product Engineering, and Data Engineering teams.
• Analytical and troubleshooting skills with a focus on reliability, operational excellence, and continuous improvement.
• Ability to simplify operational complexity through automation and reusable platform capabilities.

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