AI Engineer
Há 2 semanas
Porto, Portugal
eXalt-Fi
Tempo integral
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Our client is a leading pan-European market infrastructure, operating regulated financial markets and delivering innovative technology solutions across Europe. As part of its growing AI Team, we are looking for a hands-on AI Delivery Engineer to lead an AI delivery squad.
This is primarily an engineering role: 70–80% of your time will be dedicated to designing, building, deploying, and continuously improving production-grade AI solutions. The remaining time will focus on delivery coordination, technical guidance, backlog management, and stakeholder alignment.
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Key Responsibilities
• Design, build, test, deploy, and operate AI solutions, including agentic workflows, RAG pipelines, AI evaluation capabilities, APIs, and reusable services.
• Own the full technical lifecycle, from use-case framing and rapid prototyping to production deployment, monitoring, incident resolution, and continuous improvement.
• Write and review production code while applying best practices in testing, CI/CD, security, observability, and documentation.
• Manage the squad backlog and roadmap, ensuring clear priorities, delivery momentum, and proactive risk management.
• Guide and mentor engineers, support technical decisions, and remove delivery blockers.
• Collaborate with business and IT stakeholders to turn complex needs into pragmatic, scalable AI solutions.
• Track solution quality, adoption, user feedback, and business impact; share progress with key stakeholders. 👤 Profile & Technical Requirements
• 3–4+ years of hands-on experience delivering Generative AI solutions in a professional environment.
• Practical expertise in LLMs, agentic systems, RAG, tool calling, prompt engineering, and/or AI evaluation frameworks.
• Proven experience bringing AI solutions beyond proof of concept into production or production-like environments.
• Strong software-engineering foundations, including solution design, code review, automated testing, CI/CD, observability, and secure development.
• Experience deploying and operating cloud workloads; AWS experience is a strong advantage.
• Strong ownership, prioritisation, and problem-solving skills, with the ability to manage several use cases at different stages.
• Demonstrated technical leadership through mentoring, coordination, or influence; formal people-management experience is not required.
• Fluent written and spoken English, with excellent communication skills for technical and non-technical stakeholders.
Key Responsibilities
• Design, build, test, deploy, and operate AI solutions, including agentic workflows, RAG pipelines, AI evaluation capabilities, APIs, and reusable services.
• Own the full technical lifecycle, from use-case framing and rapid prototyping to production deployment, monitoring, incident resolution, and continuous improvement.
• Write and review production code while applying best practices in testing, CI/CD, security, observability, and documentation.
• Manage the squad backlog and roadmap, ensuring clear priorities, delivery momentum, and proactive risk management.
• Guide and mentor engineers, support technical decisions, and remove delivery blockers.
• Collaborate with business and IT stakeholders to turn complex needs into pragmatic, scalable AI solutions.
• Track solution quality, adoption, user feedback, and business impact; share progress with key stakeholders. 👤 Profile & Technical Requirements
• 3–4+ years of hands-on experience delivering Generative AI solutions in a professional environment.
• Practical expertise in LLMs, agentic systems, RAG, tool calling, prompt engineering, and/or AI evaluation frameworks.
• Proven experience bringing AI solutions beyond proof of concept into production or production-like environments.
• Strong software-engineering foundations, including solution design, code review, automated testing, CI/CD, observability, and secure development.
• Experience deploying and operating cloud workloads; AWS experience is a strong advantage.
• Strong ownership, prioritisation, and problem-solving skills, with the ability to manage several use cases at different stages.
• Demonstrated technical leadership through mentoring, coordination, or influence; formal people-management experience is not required.
• Fluent written and spoken English, with excellent communication skills for technical and non-technical stakeholders.