Senior AI Platform Engineer

Confidential; Lisboa; Presencial; EUR 90 000 - 120 000; Tempo integral

Resumo da oferta

Confidential is seeking a senior engineer to design, build, and operate an AI-native procurement platform at enterprise scale. You will deliver deployment pipelines, evaluation frameworks, and robust observability across AI services, agents, and workflows.

You will partner with ML, Platform, Product, and Data Engineering teams to ensure scalable, secure, and reliable production systems, while advancing lifecycle governance and automation throughout the AI delivery stack.

Qualificações

• Extensive experience operating production ML/AI platforms.
• Strong software engineering with automation, deployment pipelines, and tooling.
• Experience with cloud-native infrastructure, containers, and distributed systems.
• Solid understanding of CI/CD, IaC, release automation, and production operations.
• Experience implementing observability across complex distributed apps.
• Familiarity with model lifecycle management, deployment strategies, experimentation, and production evaluation.
• Ability to collaborate across Platform, ML, Product, and Data Eng teams.
• Strong analytical and troubleshooting skills focused on reliability and continuous improvement.

Responsabilidades

• Design, build, and maintain platform capabilities for deploying and operating production AI systems.
• Develop automated pipelines for packaging, deployment, versioning, testing, rollout, and lifecycle management.
• Build evaluation frameworks measuring model quality, retrieval effectiveness, latency, cost, and business impact.
• Implement monitoring for models, inference services, retrieval pipelines, and AI agents using metrics, logging, tracing, and telemetry.
• Collaborate with ML Engineers to productionize models and improve deployment reliability.
• Work with Platform Engineering to optimize runtime environments, scaling, security, and resilience.
• Develop deployment strategies supporting experimentation, canary releases, A/B testing, and progressive rollouts.
• Implement governance controls for reproducibility, versioning, auditability, and compliance.
• Automate repetitive operational tasks to boost productivity and deployment confidence.
• Contribute to ASDLC by embedding evaluation, automation, and operational intelligence into delivery processes.

Conhecimentos

ML Platform Ops

Automation

CI/CD

Kubernetes

Observability

Distributed systems

Troubleshooting

Ferramentas

Terraform

Docker

Cloud platforms

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.

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.

What you will work with

• Machine learning deployment pipelines
• Inference services and model serving
• Evaluation frameworks and benchmarking
• Observability, logging, metrics, and distributed tracing
• Cloud-native infrastructure and Kubernetes
• CI/CD and engineering automationRetrieval and inference pipelines
• AI agents and orchestration services
• Engineering productivity tooling

Requirements

• Extensive experience operating production machine learning or AI platforms.
• Strong software engineering skills with experience building automation, deployment pipelines, and operational tooling.
• Experience with cloud-native infrastructure, container platforms, and distributed systems.
• Strong understanding of CI/CD, infrastructure as code, release automation, and production operations.
• Experience implementing observability across complex distributed applications.
• Understanding of model lifecycle management, deployment strategies, experimentation, and production evaluation.
• Experience collaborating across Platform Engineering, Machine Learning, Product Engineering, and Data Engineering teams.
• Strong 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.

Why this role matters

Reliable AI products require far more than good models. They require robust operational platforms that continuously evaluate, deploy, monitor, and improve intelligent systems throughout their lifecycle.

This role ensures AI capabilities can be delivered with the same reliability, repeatability, and engineering discipline as the rest of the platform. By building automated deployment, evaluation, and operational capabilities, the role enables engineering teams to move quickly while maintaining quality, resilience, and trust in production AI systems.