Atidot is seeking an Intermediate AI Engineer to join our dynamic team, focusing on developing and implementing AI solutions that enhance our product offerings and drive innovation in the insurance technology sector.
You’ll Do
- Own ML models for retention and growth, from design, through experimentation on large temporal tabular datasets, to implementation, including performance tuning and monitoring.
- Build decision-support systems that combine classical ML models with LLM/RAG components to generate actionable insights.
- Maintain and develop leakage-safe data pipelines, time-aware validation schemes, and robust evaluation methodologies.
- Collaborate closely with data scientists, engineers, and product stakeholders to translate business questions into concrete AI solutions.
Requirements:
What We’re Looking For
- 3+ years of hands-on experience building and deploying modular, end-to-end ML and AI systems in Python.
- Strong experience with and understanding of gradient boosting and ensembling methods, and deep acquaintance with scikit-learn-type pipelines.
- Solid understanding of feature engineering and selection, leakage detection and prevention, and evaluation under class imbalance.
- Strong experience integrating LLMs into ML workflows, including RAG-based systems, and evaluating their outputs.
- Experience with and understanding of post-hoc XAI methods, especially SHAP.
- Ability to write clean, modular, reproducible code and reason about trade-offs under real constraints (runtime, data quality, ambiguity).
- Comfortable working independently and taking ownership, while communicating clearly with teammates.
Nice to Have (but Not Required)
- Experience with Optuna or similar hyperparameter optimization tools.
- Experience with SHAP or other explainability methods for tree-based models.
- Familiarity with AWS (S3, EC2, batch workflows, etc.).
Tags & focus areas
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