IND-Staff Engineer

About the role

IND Staff Engineer - GCC094 We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future. Experience in statistical modeling and machine learning using Python, including extensive use of pandas, NumPy, scikit-learn, and strong SQL for data exploration, feature development, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred. Experience across the end-to-end modeling lifecycle, including problem framing and requirements gathering, experiment design, offline evaluation, and ongoing production validation and monitoring. Solid understanding and practical application of core machine learning methods, with 3+ years of experience applying deep learning architectures in real-world use cases. Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking (e.g., classification, forecasting, ranking/IR, and business KPIs), and supporting A/B testing, drift detection, and performance regression monitoring. Experience working with unstructured data, including document parsing and OCR fundamentals, text normalization, metadata and lineage awareness, and PII detection or redaction considerations. Experience using Git and Unix-based development environments, with experience building reproducible notebooks or pipelines and ensuring repeatable analytical workflows; 3+ years of exposure to basic container and cloud fundamentals supporting deployment workflows Experience communicating modeling decisions, design tradeoffs, evaluation results, and risks to both technical and non-technical audiences, and translating analytical outcomes into measurable business impact. Experience working with cloud-based AI platforms such as Google Vertex AI, AWS SageMaker or Bedrock, or Azure AI Services, supporting experimentation, model training, and deployment. Experience deploying models and integrating scoring logic into production systems, including operation within complex enterprise or packaged application environments (e.g., Duck Creek, Ratabase). Experience with NLP and Generative AI capabilities, including embeddings, retrieval strategies (dense and hybrid), chunking approaches, prompt engineering, structured outputs, and contributing to Retrieval-Augmented Generation (RAG) solutions and evaluations. Experience or exposure to advanced GenAI applications and extensions, such as agent or tool-use concepts, domain-specific knowledge graph integration, synthetic data generation, sentiment modeling, and GenAI use cases in filing or compliance contexts. Experience working within enterprise AI governance expectations, including aligning model development with compliance, privacy, documentation, and ethical standards. About Us | Our Culture | What It's Like to Work Here