Machine Learning Engineer (All Levels)

- Bachelors degree, preferably in a STEM field. - Entry-Level: 0–2 years of relevant experience (academic, internship, or professional). - Mid-Level: 3+ years of hands-on experience in developing ML solutions. - Senior-Level: 3+ years of experience in deploying and operating machine learning systems. - Proficient in Python, including libraries such as pandas, numpy, and scikit-learn. - Familiarity with ML libraries like XGBoost, LightGBM, and experience with PyTorch/TensorFlow is beneficial. - Solid understanding of SQL for data exploration and feature engineering. - Knowledge of model evaluation techniques and interpretability methods (e.g., SHAP). - Eagerness to learn technologies such as Terraform, Java, and Typescript with no prior experience needed. - Strong communication skills and ability to collaborate effectively with both technical and non-technical stakeholders. - Leadership and mentoring capabilities for senior roles. - Preferred: Experience with Spark, distributed computing, APIs, containers, CI/CD, and monitoring solutions. - Familiarity with platforms such as MLflow, SageMaker, Azure ML, Docker, along with cloud experience in AWS, Azure, or GCP. - Background in deep learning, NLP, computer vision, or ownership of end-to-end ML products is advantageous. - Experience within the insurance or financial services sector is a plus.