AI Engineer | Insurance

Key Responsibilities Build multi-agent workflows to optimize business processes within insurance. Develop and maintain software pipelines for data processing, model training, and deployment using Python-based frameworks. Train, fine-tune, and optimize neural network models, including LLMs, for specific use cases such as risk assessment and customer query resolution. Collaborate with data scientists, engineers, and business stakeholders to translate requirements into production-ready AI applications. Ensure AI models adhere to banking regulations, data privacy standards (e.g., GDPR, CCPA), and ethical AI practices. Monitor deployed solutions in production, troubleshooting issues, and implement updates as needed. Strong experience delivering production-grade applications , backend services and technical components that support data, workflow, automation or AI-driven solutions. Participate in code reviews, agile ceremonies, and CI/CD processes. Experience and Qualifications 3–12 years of professional experience in AI/ML engineering , with a focus on software development and deployment. Build and integrate components for LLM , RAG , search , agent-based and automation solutions, Strong software engineering experience with Python, Java, React Next, Node.js Hands-on experience training and fine-tuning neural network models, with a track record of optimizing for performance and scalability. Proven expertise working with LLMs (e.g., GPT, BERT ) and building agentic solutions using frameworks like LangChain, LangGraph, or ADK. Familiarity with cloud platforms (AWS, Azure, or Google Cloud) for model deployment and data management. Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics, and bias mitigation. Experience with version control systems (e.g., Git) and agile methodologies. Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment. Preferred Qualifications Experience in financial services domains Familiarity with big data tools (e.g., Spark, Hadoop) and databases (SQL/NoSQL). Knowledge of graph databases and knowledge graphs. Understanding DevOps practices, containerization (Docker, Kubernetes), and MLOps workflows. Publications, open-source contributions, or certifications in AI/ML. Strong communication skills for presenting technical concepts to non-technical stakeholders.