AI Delivery Engineer - Squad Lead

Location: Portugal, Porto

Company

Build production-grade AI solutions across hirify.global in collaboration with business and IT teams.

What you will do

• Design, implement, test, deploy, and operate production-grade AI solutions, including agentic systems, retrieval-augmented generation pipelines, evaluation capabilities, APIs, and reusable services.
• Write and review production code, troubleshoot technical issues, and improve system performance, reliability, security, and maintainability.
• Own delivery across the full lifecycle, from prototypes and architecture decisions to monitoring, incident resolution, and continuous improvement.
• Manage the squad backlog and use-case roadmap, coordinate dependencies, and align priorities with peer squad leads and the Head of AI.
• Guide engineers, mentor less experienced colleagues, unblock delivery, and support onboarding.
• Work with business and IT stakeholders to frame use cases, define scope, track impact, and communicate progress and risks.

Requirements

• 3–4 years of hands-on experience building and delivering generative AI solutions in a professional environment.
• Experience taking AI solutions beyond prototypes into production or production-like operation.
• Strong software-engineering foundations, including solution design, code review, testing, CI/CD, observability, documentation, security, and AI evaluation.
• Practical experience with agentic workflows, tool use, retrieval-augmented generation, prompt design, or evaluation frameworks.
• Experience deploying and operating cloud-based workloads; AWS experience is preferred.
• Strong ownership, prioritization, stakeholder communication, technical guidance, mentoring, and coordination skills. Formal people-management experience is not required.
• Clear written and spoken English is required.

Culture & Benefits

• Hands-on engineering represents approximately 70–80% of the role.
• Collaborate in a small squad with business, IT, peer squad leads, and the Head of AI.
• Contribute to shared engineering practices, reusable components, and technical standards across the AI team.
• Support the wider organization's AI transformation through practical implementations and knowledge sharing.