4 days ago
Boston, MA, USASenior
Responsibilities
- Design, build, and deploy machine learning models and AI-powered features into production SaaS products.
- Maintain scalable pipelines for ingesting, transforming, and enriching 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.
- Own data quality, observability, and monitoring for production AI/ML workloads.
- Design and implement cloud-native microservices and APIs using Python and C#/.NET on AWS.
- Contribute to requirements review, design, development, testing, and deployment across the SDLC.
- Conduct code reviews, mentor team members, assess technical risks, and contribute to roadmap and technology feasibility planning.
Requirements
- Bachelor of Science in Computer Science, Mathematics, Statistics, or a related quantitative field is required; an M.S. or Ph.D. is preferred.
- At least 5 years of software engineering experience, including 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, including training, evaluation, versioning, and serving.
- Proficiency with AWS SageMaker, S3, Glue, Athena, Lambda, EC2, and CloudWatch.
- Experience with Apache Spark, Airflow, dbt, or equivalent data pipeline tooling.
- Understanding of data modeling, SQL, and large-scale databases such as PostgreSQL or MSSQL.
- Knowledge of software engineering fundamentals, CI/CD, DevOps, testing, and system design.
- Familiarity with REST API design, microservices, and containerization using Docker and Kubernetes.
- Experience with Agile development methodologies.
- Preferred qualifications include LLMs, prompt engineering, RAG, MLflow, Weights & Biases, AWS certification, geospatial or catastrophe data, climate/weather datasets, Angular, React, .NET Core, or insurance, reinsurance, or financial services experience.
Benefits
- Flexible hybrid work model with the role based in the Boston office.
- Health insurance, a retirement plan, disability benefits, and paid time off.
- Work flexibility plus coaching and training opportunities.
Tech Stack
AngularApache AirflowApache SparkAWSC#dbtDockerKubernetesMicrosoft SQL ServerMLflow.NETPostgreSQLPythonPyTorchReactscikit-learnSQLTensorFlowXGBoost
Categories
About Verisk
Verisk builds data analytics platforms, predictive models, and software used by property and casualty insurers to underwrite, price, detect fraud, and manage claims. The company monetizes industry-scale datasets, catastrophe risk models, and workflow tools sold by subscription and transaction to insurers, reinsurers, and brokers worldwide. Founded in 1971 and headquartered in Jersey City, New Jersey, Verisk is a public company trading on Nasdaq (VRSK).