Architect and design machine learning systems capable of processing millions of real-time data points, leveraging feature stores, real-time inference pipelines, and scalable model serving frameworks to ensure high performance and low latency
Drive architectural decision-making for data and ML systems through RFC processes, ensuring solutions are scalable, statistically sound, and future-proof
Contribute hands-on to data and ML challenges, including building data architectures and high-performance feature engineering pipelines
Collaborate closely with DevOps teams to ensure ML infrastructure (Kubernetes, cloud platforms, GPU clusters) is optimized for training and inference workloads
Define and evolve scalable data architectures that support advanced analytics, predictive modeling, and business growth
Mentor and guide Senior Data Scientists and ML Engineers, fostering strong practices in statistical rigor, MLOps, and systems thinking
Support other tasks or projects as assigned to meet team and business needs

Degree in a STEM field such as Computer Science, Engineering, or Applied Mathematics, or equivalent practical experience
5+ years of combined experience across Data Engineering, Data Architecture, and Data Science
Proven experience designing and deploying large-scale, distributed data systems handling high transaction volumes
Strong expertise in cloud environments such as AWS, GCP, or Azure, and modern data platforms such as Snowflake, Databricks, or BigQuery
Solid understanding of data modeling principles, including relational, dimensional, and NoSQL approaches
Experience building and orchestrating data pipelines using tools such as Airflow, dbt, Spark, or Kafka
Knowledge of data governance, security, and compliance best practices
Advanced proficiency in Python and SQL, with experience using data science libraries such as pandas, NumPy, and scikit-learn
Proven track record of building, training, and deploying machine learning models to solve real-world business problems
Experience applying MLOps principles to move models from experimentation to production-ready systems
Experience developing billing and rating systems for AI-driven or consumption-based models would be considered an advantage
Hands-on experience integrating AI services or building advanced AI solutions such as RAG pipelines or API-based AI workflows would be considered an advantage

Work from anywhere
this is a remote opportunity with a primary focus on candidates based in the EU due to team needs and coverage
A competitive salary that values you and your unique skill sets
Career advancement & professional development opportunities to help you reach your full potential
Flexible work arrangements to support work/life balance

Principal Data Architect

Key skills