Description
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.
Responsibilities
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 SageMaker, 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
Qualifications
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, PyTorch, TensorFlow, or XGBoost
- Experience building and deploying production ML systems — model training, evaluation, versioning, and serving
- Proficiency with AWS data and AI services: SageMaker, S3, Glue, Athena, Lambda, EC2, CloudWatch
- 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
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