My responsibilities:

• Collaborates with Data Scientists to validate and scale new algorithms, spanning classical machine learning, deep learning, and LLM-based approaches, through pilot phases, and later industrializes these solutions at scale.
• Designs, builds and operates Generative AI / LLM applications (e.g., RAG pipelines, fine-tuned models, agentic workflows), applying LLMOps practices such as prompt and version management, systematic evaluation, guardrails, and cost/latency optimization
• Influences, contributes and maintains the large-scale data infrastructure required for the AI projects in close collaboration with the data engineers
• Leverages an understanding of software architecture and software design patterns to write scalable, maintainable, well-designed and future-proof code
• Designs, develops and maintains the framework for analytical and ML pipelines, applying MLOps practices: CI/CD for models, automated training and retraining workflows, model registry and reproducible deployments
• Develops common components to address pain points in machine learning projects, such as model lifecycle management, feature stores, data quality evaluation, and evaluation harnesses for LLM-based applications
• Implements monitoring and observability for models in production, including tracking data and model drift, performance degradation, and, for GenAI applications, output quality and hallucination.
• Supports responsible AI practices: contributes to model risk management, explainability, and compliance with applicable AI regulation (e.g., EU AI Act) and internal governance standards
• Provides input and helps implement frameworks and tools to improve data quality
• Works in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, designers, product managers and others to build the AI ecosystem within the Group
• Delivers on time, demonstrating strong commitment to deliver on the team mission and agreed backlog

The knowledge I own:

• Has a background in computer science, mathematics or related technical discipline
• Is experienced in software engineering with exposure to statistical, data science and AI/ML roles
• Has deep knowledge and proven experience with optimizing and operating machine learning models in a production context; hands-on experience deploying and operating LLM-based applications is a strong plus
• Worked with Python in a productive environment (mandatory). Background in programming in C, C++, Java and Scala is beneficial. Exposure to both streaming and batch analytics. Experience with the core data/ML stack is beneficial: SQL, Spark, Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, Databricks
• Experience with MLOps tooling is beneficial: MLflow or comparable experiment tracking and model registry, workflow orchestration (e.g., Azure Data Factory, Databricks Declarative Automation Bundles), containerization and orchestration (Docker, Kubernetes), CI/CD, infrastructure-as-code, and cloud ML platforms
• Experience with the GenAI/LLM ecosystem is beneficial: Hugging Face, LLM APIs, orchestration frameworks (e.g., LangChain, LlamaIndex), vector databases (e.g., pgvector, Azure/Databricks AI Search), and LLM evaluation/observability tooling
• Has experience working with large data sets, simulation/optimization and distributed computing tools (e.g., Spark, Ray)

Agile / Digital Experience

• Has experience working in AI startup environment or organizations with an agile culture
• Has a professional attitude and service orientation; superb team player

Individual Skills

• Has sound problem-solving skills with the ability to quickly process complex information and present it clearly and simply
• Demonstrates good written and verbal communication skills along with strong desire to work in cross-functional teams

Mindset & Behaviors

• Is able to build a sense of trust and rapport in terms of quality
• Has an attitude to thrive in a fun, fast-paced, startup-like environment
• Is open minded to new approaches and learning

The offer that would convince me:

• We develop and maintain our own product with high emphasis on quality and long-term stability
• Code quality matters: we follow Clean Code and SOLID principles backed by robust testing
• Flexible working hours and remote work options
• Competitive salary with regular adjustments based on inflation and loyalty
• Access to continuous learning via our internal learning platform and expert communities

Online application:

Please use our online application and attach your resume.

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