AI Engineer – Earnix

Location: Remote
Duration: 12+ Months (Contract)

We are seeking an experienced AI Engineer with strong expertise in Earnix, Artificial Intelligence, and Machine Learning to support the design, development, and optimization of intelligent pricing and decisioning solutions for the insurance or financial services industry. The ideal candidate will work closely with business stakeholders, data scientists, and engineering teams to build AI-driven models and integrate them with the Earnix platform.

• 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.
• Stay current with advancements in AI, machine learning, pricing optimization, and Earnix product capabilities.

• 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.

• 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.