Key skills
ML systems architectureML lifecycle automationAWS experienceInfrastructure-as-code (Terraform)Python proficiencySQL skillsFeature store implementationData orchestration toolsML observability toolingCollaboration skillsPythonSQLAIMachine LearningMLLLMSnowflakeKafkaAirflowAWSTerraformECSLambdaSageMakerBedrockKinesisPrometheusDatadogCI/CD
About this role
Built is an AI-powered platform transforming the way real estate is financed, developed, and managed. The Senior ML Ops Engineer will build the infrastructure to deploy and scale machine learning models, enabling new data products and improving operational efficiency.
Responsibilities:
- 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
- Experience implementing feature stores or model registries
- Familiarity with data orchestration tools (Airflow, Prefect, Dagster)
- Experience with ML observability tooling (Datadog, Prometheus)
- Experience in regulated or financial data environments
- Experience optimizing ML workloads for cost and scale
- Exposure to Snowpark, Bedrock, or LLM orchestration frameworks