AI Solutions Engineer (LLM)

TL;DR

AI Solutions Engineer (LLM): Designing and implementing AI-driven products and agentic systems with an accent on RAG pipeline architectures and context engineering. Focus on building reusable AI assets, optimizing LLM performance via advanced prompting, and integrating knowledge graphs for enterprise scale.

Location: Porto, Portugal

Company

hirify.global is a global firm specializing in AI, Data, Digital Commerce, and Advisory services, operating through a long-term partnership model to drive business growth.

What you will do

• Design, implement, and refine AI solutions across the full lifecycle from prototyping to production deployment.
• Develop the organization's context layer using knowledge graphs, ontologies, and RAG pipeline architectures.
• Apply advanced prompt, context, and memory engineering techniques to maximize LLM performance and reliability.
• Translate complex business requirements into intelligent automation use cases and AI-transformed workflows.
• Design robust, scalable agentic AI systems and orchestration architectures aligned with enterprise governance.
• Document methodologies and prompt libraries to build reusable institutional knowledge.

Requirements

• 1-4 years of professional experience as a software, data, or AI engineer.
• Strong programming skills (Python preferred) and proficiency with Git.
• Hands-on experience building LLM-based systems using frameworks like LangChain, LlamaIndex, or LangGraph.
• Solid understanding of Retrieval-Augmented Generation (RAG) and general data science principles.
• Fluency in English is required.
• Must be based in Porto, Portugal.

Nice to have

• Practical experience with knowledge graphs, ontologies, or semantic data models.
• Experience working in international, consulting, or scale-up environments.

Culture & Benefits

• A pioneering role with the scope to define AI practices within a large organization.
• Deep technical immersion across the full AI stack, including agentic systems and LLM orchestration.
• Collaborative environment working with multidisciplinary experts on a global scale.
• Culture of experimentation, continuous learning, and knowledge sharing.
• Strong commitment to diversity, equity, inclusion, and belonging (DEIB).