AI Engineer
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 design and build the technical backbone that powers a large-scale enterprise AI transformation.
A role at the intersection of cloud infrastructure, data engineering, and applied AI, with real architectural ownership.
Regular collaboration with senior technical and business stakeholders across a complex, international organization.
Exposure to a wide variety of AI tools, platforms, and frameworks in a fast-evolving technology landscape.
A dynamic, diverse team environment with strong peer learning and knowledge-sharing culture.
What you will do
Ensure the technical consistency of AI solutions and their portability across target environments, acting as the custodian of architectural standards and best practices.
In collaboration with data, IT, and business stakeholders, establish robust data and technology foundations that guarantee
scalability, reliability, security, and trust in AI solutions.
Advise on solution design and target architecture for AI applications, guiding teams towards sound, future-proof technical choices — including build or buy, models, vector stores and orchestration frameworks.
Embed security, data privacy and responsible-AI requirements (e.g. GDPR, EU AI Act) in the reference architectures.
Contribute to drafting technical specifications, follow up on development progress, and oversee acceptance testing to ensure solutions meet quality and performance standards.
Oversee the seamless integration, management, and scaling of AI systems, coordinating between data teams, IT, and business functions.
Define how AI solutions are monitored, cost-controlled and kept reliable in production, including model updates and dependency management, and make sure delivery teams meet that standard.
Participate in, and potentially lead, dedicated project governance and technical forums, ensuring architectural decisions are well-documented and understood.
Proactively identify technical risks and propose mitigation strategies to protect delivery timelines and solution integrity.
What you will bring
3 years of professional experience in software engineering, data/ML engineering, cloud engineering or IT architecture, including hands-on experience designing cloud-based systems that run in production.
Experience builduing or contributing to an LLM-based solution (RAG, agents, orchestration frameworks), understanding of its components
(model serving, data pipelines, vector databases), and usage of AI-assisted development tools in your daily work (e.g. Claude Code,
GitHub Copilot, Cursor).
Experience contributing to architectural standards and reference architectures, with the ability to review and challenge technical decisions made by other engineers or vendors.
Experience with at least one major cloud provider (Azure, AWS or GCP), including CI/CD, infrastructure-as-code and containerisation.
Solid software development skills, with the ability to confidently read and review code (Python preferred), and communicate technical constraints and risks clearly to business and project stakeholders in fluent English.
Nice to have
Experience in Azure and Databricks.
Hands-on experience in DevOps or platform engineering experience (e.g. Terraform or Bicep, Kubernetes, Azure DevOps or GitHub Actions).
Experience in data architecture: data warehousing, data lakes or lakehouse, governance, frameworks.
Experience with LLMOps practices, including evaluation, guardrails, prompt and model versioning, and monitoring of LLM-based applications.
Knowledge of security principles and AI governance frameworks, including GDPR and the EU AI Act.
Experience in international, consulting, or scale-up environments.