Remote Frontend Software Engineer

Job 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.

• 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

• 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, PyTorch, 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, Dev Ops, 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