AI/ML Engineer

Job Summary

We are looking for an AI/ML Engineer with strong Insurance domain experience to design, develop, and deploy machine learning and AI solutions for insurance-related business problems. The candidate will work with business and technical teams to build predictive models, automate processes, analyze insurance data, and improve decision-making.

Key Responsibilities

• Develop and implement Machine Learning and AI models for insurance use cases.
• Work with large and complex insurance datasets to identify patterns and insights.
• Build predictive models for areas such as claims, underwriting, risk assessment, fraud detection, and customer analytics.
• Perform data preprocessing, feature engineering, model training, evaluation, and optimization.
• Develop and maintain ML pipelines for model deployment and monitoring.
• Collaborate with Data Scientists, Data Engineers, Business Analysts, and Insurance SMEs.
• Deploy AI/ML solutions into production environments and troubleshoot model-related issues.
• Apply NLP, Generative AI, or other AI techniques where applicable.
• Ensure models are scalable, reliable, and aligned with business requirements.
• Document models, processes, and technical solutions.

Required Skills

• 7+ years of experience in AI/ML Engineering, Machine Learning, or Data Science.
• Strong experience with Python and ML libraries such as Scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch.
• Experience with Machine Learning algorithms, predictive modeling, and statistical techniques.
• Experience with data preprocessing, feature engineering, and model evaluation.
• Knowledge of ML deployment, APIs, and MLOps concepts.
• Experience working with SQL and databases.
• Good understanding of cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong Insurance domain experience is required.

Insurance Domain Experience

Experience with one or more of the following is preferred:

• Property & Casualty (P&C)
• Life Insurance
• Health Insurance
• Claims Processing
• Underwriting
• Risk Assessment
• Fraud Detection
• Policy Management
• Premium/Pricing Analytics
• Customer/Agent Analytics

Preferred Skills

• Experience with Generative AI / LLMs / NLP.
• Knowledge of MLOps and CI/CD.
• Experience with Docker/Kubernetes.
• Experience with cloud-based AI/ML services.
• Strong communication and problem-solving skills.