AI Solutions Engineer

About the role

Join Metyis' fast‑growing Data & Analytics team in Porto as an AI Solutions Engineer. You will work on cutting‑edge AI projects, turning business challenges into intelligent, production‑ready solutions.

Key responsibilities

• Design, implement, and continuously improve AI solutions across the full lifecycle, from prototype to production.
• Collaborate with central and local data teams to build and maintain the organization’s context layer, including knowledge graphs, ontologies, governance graphs, data‑lineage frameworks, and Retrieval‑Augmented Generation (RAG) pipelines.
• Apply advanced prompt, context, memory, and harness engineering techniques to optimise model performance and reliability.
• Translate business requirements into AI‑driven use cases and workflows, identifying opportunities for intelligent automation.
• Stay abreast of the latest AI models, tools, and frameworks, and integrate relevant innovations into the team’s practice.
• Contribute to the design of agentic AI systems and orchestration architectures that meet enterprise governance standards.
• Document methodologies, prompt libraries, and context‑engineering standards to create reusable institutional knowledge.

Required profile

• 1‑4 years of professional experience in AI, data engineering, or related fields.
• Strong analytical mindset with the ability to convert business problems into technical solutions.
• Excellent communication skills and a collaborative attitude.

Required skills

• Knowledge graphs and ontologies
• Data‑lineage and governance frameworks
• Retrieval‑Augmented Generation (RAG) pipelines
• Prompt engineering, context engineering, memory engineering
• LLM orchestration and agentic AI system design

What we offer

• Pioneering role in a rapidly evolving AI discipline.
• Deep technical immersion across the full AI stack.
• Close collaboration with multidisciplinary data and business teams.
• Opportunity to build reusable AI assets that generate lasting business value.
• A culture of experimentation, continuous learning, and knowledge sharing.