Senior Software Engineer/Applied AI Scientist

As a Software Engineer at our insurance company, you will be responsible for designing and delivering AI solutions that encompass statistical modeling, machine learning, and generative/agentic AI across various pipelines and systems. Your key responsibilities will include: - Building AI solutions for RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a range of predictive modeling tools - Automating regulatory filing support through GenAI capabilities, including DOI objection response generation and legacy filing ingestion - Engineering and maintaining domain-specific knowledge bases to power generative applications in underwriting, pricing, and service - Integrating domain taxonomies, regulatory constraints, and security measures into solution designs - Collaborating with stakeholders across different departments to align initiatives with business goals and define success criteria - Owning the AI lifecycle from problem framing to deployment, including data preparation, modeling, evaluation, and observability - Designing retrieval strategies for unstructured data and creating safe tool-use policies for reliable agent behavior - Defining metrics for evaluation and monitoring across various use cases and supporting A/B testing and drift detection - Developing and validating synthetic data pipelines to accelerate convergence while preserving privacy and fidelity Required Skills & Experience: - Experience in statistical modeling and machine learning using Python, with familiarity in pandas, NumPy, scikit-learn, and strong SQL - Solid understanding and practical application of core machine learning methods, with experience in deep learning architectures - Experience in model evaluation, monitoring, and test set creation, as well as A/B testing and performance regression monitoring - Working knowledge of unstructured data, Git, Unix-based environments, and cloud fundamentals - Ability to communicate modeling decisions and outcomes to technical and non-technical audiences - Experience with cloud-based AI platforms and deploying models in production systems - Familiarity with NLP, Generative AI capabilities, and enterprise AI governance expectations Nice to Have: - Expertise in RAG, Document AI Tooling, Embedding Model Selection, Orchestration Frameworks, CloudNative ML, Responsible AI & Safety, and broader modalities such as timeseries forecasting and recommenders. As a Software Engineer at our insurance company, you will be responsible for designing and delivering AI solutions that encompass statistical modeling, machine learning, and generative/agentic AI across various pipelines and systems. Your key responsibilities will include: - Building AI solutions for RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a range of predictive modeling tools - Automating regulatory filing support through GenAI capabilities, including DOI objection response generation and legacy filing ingestion - Engineering and maintaining domain-specific knowledge bases to power generative applications in underwriting, pricing, and service - Integrating domain taxonomies, regulatory constraints, and security measures into solution designs - Collaborating with stakeholders across different departments to align initiatives with business goals and define success criteria - Owning the AI lifecycle from problem framing to deployment, including data preparation, modeling, evaluation, and observability - Designing retrieval strategies for unstructured data and creating safe tool-use policies for reliable agent behavior - Defining metrics for evaluation and monitoring across various use cases and supporting A/B testing and drift detection - Developing and validating synthetic data pipelines to accelerate convergence while preserving privacy and fidelity Required Skills & Experience: - Experience in statistical modeling and machine learning using Python, with familiarity in pandas, NumPy, scikit-learn, and strong SQL - Solid understanding and practical application of core machine learning methods, with experience in deep learning architectures - Experience in model evaluation, monitoring, and test set creation, as well as A/B testing and performance regression monitoring - Working knowledge of unstructured data, Git, Unix-based environments, and cloud fundamentals - Ability to communicate modeling decisions and outcomes to technical and non-technical audiences - Experience with cloud-based AI platforms and deploying models in production systems - Familiarity with NLP, Generative AI capabilities, and enterprise AI governance expectations Nice to Have: - Expertise in RAG, Document AI Tooling, Embedding Model Selection, Orchestration Frameworks, CloudNative ML, Responsible AI & Safety, and broader modalities such as timeseries forecasting and recommenders.