AI Solutions Engineer (Fashion)

Location: Porto, Portugal

Company

hirify.global develops solutions in Big Data, digital commerce, marketing, design, and advisory services, including data and technology capabilities for the HUGO BOSS Digital Campus.

What you will do

• Design, implement, and refine AI solutions and products from prototyping through production deployment.
• Build and maintain an AI-focused context layer with knowledge graphs, ontologies, governance graphs, data lineage frameworks, and RAG architectures.
• Apply prompt, context, memory, and harness engineering techniques to improve the performance and reliability of production AI models.
• Translate business requirements into AI-enabled use cases, workflows, and intelligent automation opportunities.
• Contribute to robust, scalable agentic AI and orchestration architectures aligned with enterprise governance.
• Document methodologies, prompt libraries, and context engineering standards for reuse across the organization.

Requirements

• 1–4 years of professional experience in software engineering, data science, data engineering, or AI engineering.
• Strong programming skills in at least one modern language, Git proficiency, and solid software engineering practices.
• Hands-on experience building LLM-based systems with prompt engineering and at least one orchestration framework such as LangChain, LlamaIndex, or LangGraph.
• Strong understanding of RAG pipelines, data quality, lineage, semantics, and governance.
• Ability to combine technical rigor with business understanding and explain complex AI concepts to non-specialist audiences.
• Fluency in English is required.

Nice to have

• Python programming experience.
• Practical experience with knowledge graphs, ontologies, or semantic data models.
• Experience with international, consulting, or scale-up environments.
• Additional language skills.

Culture & Benefits

• Opportunity to work on applied AI within a large organization and help define AI practices.
• Technical exposure across data foundations, knowledge graphs, agentic systems, and LLM orchestration.
• Collaboration with central and local data teams and business stakeholders.
• Focus on experimentation, continuous learning, knowledge sharing, diversity, equity, inclusion, and belonging.