Senior Machine Learning Operations Engineer

Built Technologies

5h•$140,000 - $210,000

About The Position

Built is investing in applied machine learning to power the next generation of data products in construction finance. We’re hiring our first dedicated Senior ML Ops Engineer to build the foundation that makes that possible. Today, our data scientists are building models. What we don’t yet have is the infrastructure, lifecycle automation, and production standards to reliably deploy and scale them. This role exists to change that. You’ll design and implement the ML Ops platform that enables training, deployment, monitoring, governance, and automation across our ecosystem. This is a 0→1 build. You’ll define tooling, establish standards, and integrate ML workloads into our AWS-native, event-driven architecture. This is not a research or modeling role. It’s a platform engineering role focused on productionizing machine learning systems. Your work will directly enable new benchmarking and anonymized data products that expand Built’s market opportunity. You’ll partner closely with Data Engineering, Data Science, and Platform teams to establish how ML systems operate across Built. This role is hands-on and foundational. You’ll be shaping how machine learning operates at Built for years to come.

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

Nice To Haves

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

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