2 months ago
Boston, MA, USASenior
Responsibilities
- Design, build, and deploy machine learning models and AI-powered features in production SaaS products.
- Maintain scalable data pipelines for ingesting, transforming, and enriching large complex datasets.
- Develop ML-serving infrastructure using AWS SageMaker, Lambda, and container-based deployment patterns.
- Apply LLM integrations, RAG architectures, and generative AI capabilities to enhance product functionality where appropriate.
- Own data quality, observability, and production monitoring for AI/ML workloads.
- Design and implement cloud-native microservices and APIs using Python and C#/.NET on AWS.
- Contribute to requirements review, system design, development, testing, deployment, roadmap planning, and technology feasibility assessments.
- Conduct code reviews, mentor team members, identify technical risks, and communicate them early.
Requirements
- Bachelor of Science in Computer Science, Mathematics, Statistics, or a related quantitative field is required; a master's degree 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 model training, evaluation, versioning, and serving.
- Proficiency with AWS data and AI services including 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.
- Strong software engineering fundamentals, including system design, testing, DevOps, and CI/CD.
- Familiarity with REST APIs, microservices, Docker, and Kubernetes.
- Experience with Agile development methodologies.
- Experience with LLMs, prompt engineering, or RAG systems is preferred.
- Familiarity with MLflow, Weights & Biases, or other ML lifecycle management tools is preferred.
- AWS certification, geospatial or catastrophe modeling experience, climate/weather dataset experience, Angular or React and .NET Core experience, and insurance, reinsurance, or financial services experience are preferred.
Benefits
- Health insurance, a retirement plan, disability benefits, and paid time off.
- Office-based Boston position with a flexible hybrid work model.
- Work flexibility plus coaching and training support.
- Competitive total rewards package with base salary determined by role, experience, skill set, and location; the specific salary amount is not stated.
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).