The company is looking to improve its machine learning lifecycle and model development and delivery best practices.
Requirements
- Comprehensive experience in Python and docker.
- Familiarity with build tooling such as bash and bazel.
- Advanced proficiency in IaC principles and tools like Terraform.
- Demonstrated expertise in designing, deploying, and managing scalable and resilient MLOps solutions on AWS.
- Applied expertise in the end-to-end machine learning lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring.
- Proficiency in designing and implementing workflows using tools like AWS Step Functions
- Experience with CI/CD tailored for machine learning systems (e.g., automating model training, validation, and deployment)
Responsibilities
- Operationalize key data science solutions that enable risk-prediction products across underwriting, pricing, claims routing, and marketing.
- Design and build ML pipelines using industry best practices, primarily leveraging AWS services like SageMaker, and integrating with tools such as MLflow for experiment tracking and data platforms like Snowflake.
- Stand-up and operate a shared feature store (Snowflake Snowpark + Kafka) that supports both batch and real-time feature retrieval.
- Own real-time inference services, exposing low-latency endpoints (SageMaker endpoints or EKS micro-services) and managing blue/green or canary deployments.
- Implement comprehensive testing strategies (including Unit, integration, data validation, model validation, and performance testing) within robust CI/CD pipelines to maintain high platform quality.
- Enable ML Governance: Manage ML models and data versioning, experiment tracking, and reproducibility.
- Implement event-driven orchestration that triggers automated retraining, evaluation, and redeployment based on data drift or business events.
Other
- Bachelor degree or equivalent relevant experience
- 8 years of industry experience with 2 years focused in MLOps and 2 years in software engineering or equivalent experience
- Excellent written and verbal communication with a strong collaborative focus.
- Occasional travel may be requested or encouraged but is not required
- Must be based in the U.S, excluding U.S. territories
