Machine Learning Engineer

Overview In this role you will design and maintain scalable data pipelines and ML workflows that support underwriting and analytics. You will build backend services and microservices to enable data ingestion, ML inference, and production-grade deployment. You will diagnose data and model issues, work with cross-functional teams, and contribute to large-scale underwriting processes. This is a hands-on role in a hybrid Boston setting with meaningful impact on risk assessment and decision support. Compensation / Benefitshealth and dental benefitsvision and mental health coveragedisability and life insuranceretirement savings plans (pension/401(k) with employer matching)paid time off (vacation, holidays, personal days)wellness programs and employee assistance Responsibilities Build, scale, and maintain data pipelines for underwriting, analytics, and ML systems Develop and deploy backend services and microservices for data ingestion, retrieval, and ML inference workflows Deploy, monitor, and maintain ML pipelines and backend services in production Perform root-cause analysis of data, model, and pipeline issues and implement fixes Collaborate with data scientists, underwriters, and engineering teams to translate business needs into technical solutions Support production releases and large-scale batch processing of underwriting workflows Key requirements Master’s degree in a relevant field + 3 years Python programming for backend, automation, or ML workloads 3 years of experience with SQL or NoSQL databases (PostgreSQL or MongoDB)3 years of experience developing or deploying ML models using PyTorch or Tensor Flow 3 years of experience building or maintaining backend APIs or microservices using FastAPI or Flask 2 years of deploying ML workflows or backend services on AWS or Azure 2 years of using Docker and Kubernetes for containerization/orchestration 2 years of CI/CD using Git Hub Actions or Team City 2 years of designing distributed or scalable systems including microservices, load balancing or distributed processingcollaboration with cross-functional teamsproblem-solving and analytical thinkingeffective communication and stakeholder management Python programmingSQL/NoSQL (PostgreSQL, MongoDB)PyTorch or Tensor Flow