AI Solutions Engineering Analyst (Fashion)
Who we are
We are Metyis, a forward-thinking, global company that develops and delivers solutions around Big Data, Digital Commerce, Marketing and Design and provides Advisory services. We have offices in 15 locations with a talent pool of 1000+ employees and more than 50 nationalities, dedicated to creating long-lasting impact and growth for our business partners and clients.
Together with HUGO BOSS, our esteemed business partner, we have embarked on a joint venture and created the HUGO BOSS Digital Campus, dedicated to increasing the data analytics, eCommerce and technology capabilities of the company and boosting digital sales. The HUGO BOSS Digital Campus employees will help create a state-of-the-art data architecture infrastructure, advanced business analytics, and the development and enhancement of HUGO BOSS’ eCommerce platform and services.
This collaborative environment will provide the capabilities required for HUGO BOSS to maximise data usage and support its growth trajectory towards becoming the leading premium tech-driven fashion platform worldwide.
What we offer
The opportunity to work in one of the most rapidly evolving areas of applied AI and develop hands-on experience with emerging AI technologies.
Exposure to the full AI stack, including data foundations, knowledge management, RAG, LLMs, and AI-powered applications.
Close collaboration with experienced data and AI professionals, as well as business stakeholders, providing opportunities to learn across both technical and business domains.
The chance to contribute to AI solutions and reusable assets that create tangible business value.
A culture of experimentation, continuous learning, and knowledge sharing within a global and diverse team.
What you will do
Support the development, testing, and continuous improvement of AI solutions and products across different stages of the lifecycle, from prototyping to deployment.
Work with central and local data teams to help prepare and maintain data and context foundations for AI use cases, including data sources, knowledge bases, and RAG pipelines.
Support the implementation and testing of prompts, context strategies, and other techniques to improve the performance and reliability of AI applications.
Work with senior team members to understand business requirements and help translate operational challenges into practical AI use cases and workflows.
Research and experiment with new AI models, tools, and frameworks, sharing relevant findings and learnings with the team.
Contribute to the development and testing of AI agents and orchestration workflows under the guidance of more experienced team members.
Help document AI solutions, methodologies, prompts, experiments, and learnings to support knowledge sharing and reuse.
Support the team in monitoring AI solutions, identifying issues, and implementing improvements based on feedback and performance.
What you will bring
1–2 years of professional experience in software engineering, data science, data engineering, AI, or a related technical field. Relevant internships or academic projects are also valued.
Basic to intermediate programming skills in at least one modern programming language; Python is a plus.
Familiarity with Git and basic software engineering practices.
Some hands-on exposure to LLM-based applications, prompt engineering, or generative AI through professional, academic, or personal projects.
Basic understanding of Retrieval-Augmented Generation (RAG) and how it can be used to connect AI models with external data.
Understanding of fundamental data concepts, including data quality, data structures, semantics, and governance.
Awareness of the current AI landscape, including LLMs, multimodal models, AI agents, and emerging AI tools and frameworks.
Exposure to knowledge graphs, ontologies, semantic data models, or orchestration frameworks is a plus.
Strong analytical and problem-solving skills, with an interest in applying technology to real-world business challenges.
Ability and willingness to learn quickly and work collaboratively with more experienced technical and business colleagues.
Good communication skills, with the ability to explain technical concepts clearly.
Fluency in English; additional languages are a plus.
Experience in international, consulting, or scale-up environments is a plus.