AI Engineer Earnix

Key Responsibilities

• Design, develop, and implement AI/ML solutions integrated with the Earnix platform.
• Configure, customize, and optimize Earnix pricing, rating, and decisioning models.
• Develop predictive models for pricing, underwriting, customer segmentation, and risk analysis.
• Collaborate with business analysts, actuaries, and product owners to translate business requirements into AI-driven solutions.
• Integrate Earnix with enterprise applications, APIs, databases, and cloud platforms.
• Build and maintain machine learning pipelines for model training, deployment, monitoring, and retraining.
• Analyze large datasets to identify trends, improve model accuracy, and generate actionable insights.
• Optimize model performance and ensure scalability, reliability, and security.
• Develop technical documentation, solution designs, and deployment guides.
• Participate in Agile ceremonies including sprint planning, code reviews, and retrospectives.
• Troubleshoot production issues and provide ongoing support for AI and Earnix applications.

Required Skills

• 5+ years of experience in Artificial Intelligence and Machine Learning.
• Hands-on experience with the Earnix platform, including pricing, rating, or decisioning solutions.
• Strong programming skills in Python.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Knowledge of statistical modeling, predictive analytics, and optimization techniques.
• Experience with SQL and relational databases.
• Familiarity with REST APIs and system integrations.
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Understanding of MLOps, model deployment, CI/CD, and version control using Git.
• Strong analytical, problem-solving, and communication skills.
• Experience working in Agile/Scrum environments.

Preferred Skills

• Experience in the insurance or financial services domain.
• Knowledge of pricing optimization, underwriting, risk modeling, or actuarial analytics.
• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI agents.
• Experience with Docker and Kubernetes.
• Familiarity with data engineering tools such as Apache Spark or Databricks.
• Experience with workflow orchestration tools such as Apache Airflow.
• Exposure to BI and visualization tools such as Power BI or Tableau.
• Understanding of MLOps platforms such as MLflow, SageMaker, or Azure Machine Learning.
• Experience with model governance, explainable AI (XAI), and Responsible AI practices.
• Relevant certifications in AI/ML, cloud platforms, or Earnix are a plus.