Senior Machine Learning Operations Engineer
Job Description:
• Build and operationalize the infrastructure that allows machine learning to run reliably in production.
• Architect and implement Built’s foundational ML Ops platform from scratch
• Define and deploy reusable patterns for model training, deployment, monitoring, and retraining
• Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking
• Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data
• Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability
• Integrate ML workloads into our event-driven architecture (Kafka, Kinesis)
• Develop observability frameworks to monitor drift, performance, latency, and model quality in production
• Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions)
• Establish security and compliance standards across ML assets, including data lineage and access control
• Mentor engineers on ML Ops patterns and deployment best practices
Requirements:
• Experience architecting and deploying ML systems in production environments
• Deep familiarity with ML lifecycle automation (training, CI/CD, deployment, monitoring)
• Strong AWS experience, particularly within ML pipelines (SageMaker preferred)
• Proven experience building infrastructure-as-code solutions (Terraform)
• Experience productionizing ML workflows end-to-end, not just optimizing existing systems
• Strong Python proficiency
• Experience integrating ML workloads with data platforms and event-driven systems
• Solid SQL skills and familiarity working with Snowflake.
Benefits:
• Competitive benefits including: uncapped vacation, health, dental & vision insurance
• 401k with match and expedited vesting
• Robust compensation package, including equity in the form of stock options
• Flexible working hours, paid family leave, ERGs & Mentorship opportunities
• Learning grant program to support ongoing professional development
Apply Now
Apply Now
• Build and operationalize the infrastructure that allows machine learning to run reliably in production.
• Architect and implement Built’s foundational ML Ops platform from scratch
• Define and deploy reusable patterns for model training, deployment, monitoring, and retraining
• Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking
• Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data
• Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability
• Integrate ML workloads into our event-driven architecture (Kafka, Kinesis)
• Develop observability frameworks to monitor drift, performance, latency, and model quality in production
• Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions)
• Establish security and compliance standards across ML assets, including data lineage and access control
• Mentor engineers on ML Ops patterns and deployment best practices
Requirements:
• Experience architecting and deploying ML systems in production environments
• Deep familiarity with ML lifecycle automation (training, CI/CD, deployment, monitoring)
• Strong AWS experience, particularly within ML pipelines (SageMaker preferred)
• Proven experience building infrastructure-as-code solutions (Terraform)
• Experience productionizing ML workflows end-to-end, not just optimizing existing systems
• Strong Python proficiency
• Experience integrating ML workloads with data platforms and event-driven systems
• Solid SQL skills and familiarity working with Snowflake.
Benefits:
• Competitive benefits including: uncapped vacation, health, dental & vision insurance
• 401k with match and expedited vesting
• Robust compensation package, including equity in the form of stock options
• Flexible working hours, paid family leave, ERGs & Mentorship opportunities
• Learning grant program to support ongoing professional development
Apply Now
Apply Now