Senior Software Engineer

Verisk

Senior Software Engineer designing and deploying machine learning systems for cloud-native SaaS at Verisk. Leading AI-powered feature development and data pipeline management within the risk solutions team.

Posted 5/6/2026full-timeBoston • Massachusetts • 🇺🇸 United StatesSenior💰 $125,000 - $150,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAngularApacheAWSCloudDockerEC2KubernetesMicroservices.NETPostgresPythonPyTorchReactScikit-LearnSDLCSparkSQLTensorflow

About the role

Key responsibilities & impact

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
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

Requirements

What you’ll need

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

Benefits

Comp & perks

Health Insurance
a Retirement Plan
Disability benefits
Paid Time Off

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata engineeringAPI developmentPythonAWSML frameworksdata modelingSQLCI/CDAgile development

Soft Skills

leadershipmentoringcommunicationproblem-solvingrisk identification

Certifications

AWS Machine Learning SpecialtyAWS Solutions Architect