Senior Software Engineer

We are hiring a Senior Software Engineer with deep expertise in AI/ML engineering and data-intensive systems to join our Catastrophic and Risk Solutions team. You will be a key technical contributor on a cross-functional Agile team building cloud-native SaaS platforms that sit at the intersection of cutting-edge science and production software.

This role goes beyond traditional full-stack development — you will design and ship AI-powered features, build data pipelines, and architect scalable ML-serving infrastructure on AWS. This role is office-based in our Boston location, which has a flexible hybrid work model. AI & Data Engineering Design, build, and deploy machine learning models and AI-powered features into production SaaS products Maintain scalable data pipelines for ingestion, transformation, and enrichment of large, complex datasets Develop model-serving infrastructure using AWS Sage.

Maker, Lambda, and container-based deployment patterns Apply LLM integrations, RAG architectures, and generative AI capabilities where appropriate to enhance product functionality Own data quality, observability, and monitoring for AI/ML workloads in production Software Engineering & Architecture Lead the design and implementation of cloud-native microservices and APIs (Python, C#/.

NET) on AWS Drive best practices in design, code quality, and system design across the team Contribute to all stages of the SDLC: requirements review, design, development, testing, and deployment Conduct code reviews and mentor team members on engineering standards Proactively identify technical risks and communicate them early to course-correct Participate in roadmap planning, scoping, and technology feasibility assessments Contribute to a culture where solving customer problems is always the highest priority Required B.

S. in Computer Science, Mathematics, Statistics, or a related quantitative field; M.S. or Ph.D. preferred 5+ years of software engineering experience, with at least 2 years in a senior or lead role on cloud-native AWS products Strong Python skills for data engineering, ML pipelines, and API development Hands-on experience with ML frameworks such as scikit-learn, Py.

Torch, Tensor. Flow, or XGBoost Experience building and deploying production ML systems — model training, evaluation, versioning, and serving Proficiency with AWS data and AI services: Sage. Maker, S3, Glue, Athena, Lambda, EC2, Cloud. Watch Experience with data pipeline tooling: Apache Spark, Airflow, dbt, or equivalent Solid understanding of data modeling, SQL, and working with large-scale databases (PostgreSQL, MSSQL, or similar) Strong grasp of software engineering fundamentals: CI/CD, DevOps, testing, and system design Familiarity with REST API design, microservices, and containerization (Docker, Kubernetes) Experience with Agile development methodologies Nice to Have Experience with LLMs, prompt engineering, or RAG (Retrieval-Augmented Generation) systems Familiarity with MLflow, Weights & Biases, or other ML lifecycle management tools AWS Certification (Machine Learning Specialty, Solutions Architect, or equivalent) Experience with geospatial data, catastrophe modeling, or climate/weather datasets Full-stack experience with Angular or React and .

NET Core Background in the insurance, reinsurance, or financial services industries #LI-LM03 #LI-Hybrid