Senior Data Engineer, MLOps
Job Description:
• 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.
• Monitor production models for performance, drift, and data quality—and drive automated remediation.
Requirements:
• Bachelor degree or equivalent relevant experience.
• 8 years of industry experience with 2 years focused on MLOps and 2 years in software engineering or equivalent experience.
• 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.
• Excellent written and verbal communication with a strong collaborative focus.
• Proficiency in designing and implementing workflows using tools like AWS Step Functions.
• Experience with CI/CD tailored for machine learning systems.
Benefits:
• We provide a wide variety of health, wellness and other benefits.
• These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents.
• A Headspace app subscription.
• Monthly wellness allowance.
• A 401(k) Plan with a company match.
• Given our virtual environment— in order to set you up for success at home, a one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion.
• All employees accrue four weeks of PTO in their first year of employment.
• New parents receive twelve weeks of fully paid parental leave which may be taken within one year after the birth and/or adoption of a child.
• Each year for professional learning, continuing education and career development, we’re committed to investing in and helping our people grow personally and professionally.
• All employees receive up to $5000.
Apply Now
Apply Now
• 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.
• Monitor production models for performance, drift, and data quality—and drive automated remediation.
Requirements:
• Bachelor degree or equivalent relevant experience.
• 8 years of industry experience with 2 years focused on MLOps and 2 years in software engineering or equivalent experience.
• 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.
• Excellent written and verbal communication with a strong collaborative focus.
• Proficiency in designing and implementing workflows using tools like AWS Step Functions.
• Experience with CI/CD tailored for machine learning systems.
Benefits:
• We provide a wide variety of health, wellness and other benefits.
• These include medical, dental, vision, life insurance and supplemental income plans for you and your dependents.
• A Headspace app subscription.
• Monthly wellness allowance.
• A 401(k) Plan with a company match.
• Given our virtual environment— in order to set you up for success at home, a one-time payment of $2K will be provided to cover the purchase of in-home office equipment and furniture at your discretion.
• All employees accrue four weeks of PTO in their first year of employment.
• New parents receive twelve weeks of fully paid parental leave which may be taken within one year after the birth and/or adoption of a child.
• Each year for professional learning, continuing education and career development, we’re committed to investing in and helping our people grow personally and professionally.
• All employees receive up to $5000.
Apply Now
Apply Now