Senior AI Platform Engineer

Confidential; Lisboa; Presencial; EUR 90 000 - 130 000; Tempo integral

Resumo da oferta

Confidential in Lisbon is seeking a senior AI Platform Engineer to design, build, and operate production AI systems at scale. You will deploy models, create robust evaluation pipelines, and implement end-to-end tooling for reliability.

You will work with ML, Platform, Product, and Data Eng teams to ensure scalable, secure, and observable AI capabilities, with an emphasis on automation, canary rollouts, and governance.

Qualificações

• 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.

Responsabilidades

• 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.

Conhecimentos

ML platforms

Automation

CI/CD

Observability

Container platforms

Distributed systems

Collaboration

Descrição da oferta de emprego

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.

You will build the engineering capabilities that take AI from experimentation into reliable production systems. This includes model deployment, inference infrastructure, evaluation pipelines, observability, versioning, performance monitoring, and automation across the AI lifecycle. You will work closely with Machine Learning, Platform Engineering, Product Engineering, and Data Engineering to ensure AI capabilities remain scalable, secure, and operationally robust.

This is a senior engineering role requiring strong software engineering, cloud platform, and operational experience combined with practical knowledge of modern AI deployment practices.

Why this role matters

Reliable AI products require more than high-performing models. They depend on robust platforms that support continuous deployment, evaluation, monitoring, and improvement throughout the AI lifecycle. This role is critical in ensuring AI capabilities are delivered with the same reliability, scalability, and engineering discipline as the broader technology platform, enabling teams to innovate quickly while maintaining quality, resilience, and trust in production environments.

Responsibilities

• 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.

Requirements

• 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.