Senior Software Engineer

Salary: $121,000 - 161,000 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.

• 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.

• 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 36 week of 2026