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

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.

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

Senior Machine Learning Operations Engineer

Key skills