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The project consists of building a scalable backend platform that runs data-driven analytical models in a controlled, automated, and auditable way. The backend must orchestrate model execution, enforce data validation, manage metadata and artefacts, and ensure full traceability of every processing step. The architecture is modular and cloud-oriented, with a strong focus on automation, versioning, and operational reliability.
Job Responsibility:
Develop Python wrappers around insurance models and register them as MLflow Projects on Databricks
Implement data-validation and metadata-tracking logic against the Data Contract Registry (PostgreSQL)
Collaborate with actuaries to translate reserving logic into reproducible, parameterized Python workflows
Contribute to CI/CD for model deployment and lifecycle management in Databricks
Ensure transparency, auditability, and reproducibility of all model runs
Cooperate with Node.js (NestJS) and React engineers on orchestration and UI integration
Requirements:
5+ years of experience in Software Engineering
3+ years of experience in Python backend or data engineering
Hands-on skills with pandas / PySpark and SQL (PostgreSQL)
Experience with model packaging or ETL/ELT pipelines
Familiarity with cloud environments (Azure preferred)
Ability to write clean, testable, well-documented code
English fluency for daily communication with actuarial and engineering teams
Nice to have:
Prior experience in insurance or actuarial analytics
Knowledge of MLflow model management and Databricks Jobs
What we offer:
Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation
Be part of a global team with equal opportunities for collaboration across continents and cultures
Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards