Senior Software Engineer

Salary: $114000 - 151000 per year Requirements: We need a B.S. in Computer Science, Mathematics, Statistics, or another quantitative discipline; an M.S. or Ph.D. is preferred. We need 5+ years of software engineering experience, including at least 2 years in a senior or lead capacity on cloud-native AWS products. We need strong Python expertise for data engineering, ML pipelines, and API development. We need hands-on experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost. We need experience building and operating production ML systems, including training, evaluation, versioning, and serving. We need proficiency with AWS data and AI services such as SageMaker, S3, Glue, Athena, Lambda, EC2, and CloudWatch. We need experience with data pipeline tools such as Apache Spark, Airflow, dbt, or similar platforms. We need a solid understanding of data modeling, SQL, and large-scale databases such as PostgreSQL or MSSQL. We need a strong foundation in software engineering practices, including CI/CD, DevOps, testing, and system design. We need familiarity with REST API design, microservices, and containerization technologies such as Docker and Kubernetes. We need experience working in Agile development environments. Bonus points if you have experience with LLMs, prompt engineering, or RAG systems. Bonus points if you have used MLflow, Weights & Biases, or comparable ML lifecycle management tools. Bonus points if you hold an AWS certification in Machine Learning Specialty, Solutions Architect, or an equivalent area. Bonus points if you have worked with geospatial data, catastrophe modeling, or climate and weather datasets. Bonus points if you bring full-stack experience with Angular or React and .NET Core. Bonus points if you have experience in insurance, reinsurance, or financial services. Responsibilities: We design, build, and deploy machine learning models and AI-powered capabilities into production SaaS products. We maintain scalable data pipelines for ingesting, transforming, and enriching large, complex datasets. We develop model-serving infrastructure using AWS SageMaker, Lambda, and container-based deployment patterns. We apply LLM integrations, RAG architectures, and generative AI features where they add value to the product. We own data quality, observability, and monitoring for AI/ML workloads in production. We lead the design and implementation of cloud-native microservices and APIs using Python and C#/.NET on AWS. We promote best practices in design, code quality, and system architecture across the team. We contribute across the full SDLC, from requirements review and design through development, testing, and deployment. We perform code reviews and mentor teammates on engineering standards. We identify technical risks early and communicate them to help keep delivery on track. We take part in roadmap planning, scoping, and feasibility assessments for new technology. We help foster a culture where solving customer problems remains the top priority. Technologies: AI Airflow API AWS Lambda Angular Architect C# CI/CD Cloud CloudWatch DevOps Docker EC2 Kubernetes LLM Machine Learning PostgreSQL PyTorch Python REST React SQL Spark TensorFlow dbt microservices ASP.NET More: We are hiring for our Catastrophic and Risk Solutions team, where we build cloud-native SaaS platforms at the intersection of advanced science and production software. This is an office-based role in our Boston location, supported by a flexible hybrid work model. We offer the opportunity to make a meaningful technical impact on AI-driven products in a cross-functional Agile environment while collaborating with a team focused on innovation, quality, and customer outcomes. last updated 28 week of 2026