Senior AI/ML Engineer

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more.

About the role

We are looking for a Senior AI/ML Engineer to join the team responsible for the end-to-end delivery of AI and Generative AI solutions - from ideation and experimentation to production operations and continuous improvement. This is a senior technical role combining hands‑on machine learning engineering with architecture, technical strategy, AI governance, and technical leadership. You will lead complex AI initiatives spanning multiple models, domains, and products, helping define the engineering standards and development patterns that enable scalable, maintainable, and responsible AI solutions. The solutions you will work on will support dealer operations, back-office processes, document processing, decision‑support systems, and intelligent data platforms. You will be:

You will be

• leading the design and delivery of complex machine learning and AI solutions aligned with business objectives.
• defining ML architecture standards, development patterns, and engineering best practices across AI initiatives.
• driving technical strategy and technology selection decisions.
• designing and overseeing end-to-end ML solutions covering data preparation, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
• building and deploying production‑grade ML models using modern ML frameworks and cloud‑based platforms.
• working closely with Data Engineers, MLOps Engineers, Solution Architects, and business stakeholders to ensure scalable and production‑ready solutions.
• driving industrialization practices including CI/CD, observability, monitoring, model lifecycle management, and operational excellence.
• providing technical leadership across delivery teams and mentor other AI/ML Engineers.
• acting as a trusted technical advisor to business stakeholders and solution leadership.
• driving AI governance and responsible AI practices across solutions.
• overseeing AI Act compliance activities, including risk classification, transparency mechanisms, technical documentation, event logging, and audit readiness.
• supporting production troubleshooting and ensure the long‑term maintainability and sustainability of AI solutions.
• contributing to knowledge sharing and capability building within the client’s internal AI and Automation teams.

Work from the European Union region and a work permit are required.

Your profile

• 6+ years of professional experience in Machine Learning / AI Engineering.
• strong Python programming skills.
• hands‑on experience with PyTorch and/or TensorFlow and scikit‑learn.
• proven experience building and deploying machine learning models in production environments.
• strong understanding of model evaluation, experimentation, performance monitoring, and ML lifecycle management.
• experience with MLflow or similar ML lifecycle management platforms.
• experience with cloud‑based ML platforms - Azure ML preferred; Vertex AI or AWS SageMaker also welcome.
• strong understanding of MLOps and CI/CD practices.
• experience working with structured and unstructured data.
• understanding of AI governance, responsible AI principles, model documentation, and compliance requirements.
• experience collaborating with cross‑functional Agile teams and working directly with business stakeholders.
• strong analytical and problem‑solving skills.
• excellent communication and stakeholder management skills.
• experience providing technical guidance or mentoring to other engineers.

Nice to have

• experience with GenAI solutions and LLM‑based applications.
• experience with vector databases and embedding models.
• strong knowledge of Retrieval‑Augmented Generation (RAG) architectures.
• experience with document intelligence and OCR solutions.
• knowledge of Azure OpenAI services.
• experience in automotive, mobility, retail, or dealer‑network environments.
• familiarity with blue/green, canary, rolling, or shadow deployment strategies.
• experience supporting AI systems in regulated environments.

Recruitment Process

CV review – HR call – Interview – Client Interview – Decision