Senior Machine Learning Operations Engineer Remote

Senior Machine Learning Operations Engineer

Remote - USA

Built's Mission: Connect and simplify doing business in real estate. Built is the AI-powered platform transforming the way real estate is financed, developed, and managed. Purpose-built for real estate and construction, Built began by fixing construction draw management for lenders and has grown into a comprehensive operating system addressing some of the industry’s most complex challenges. Through its connected product suite, Built enables stakeholders to finance, develop, build, own, and operate smarter—all in one place.

The platform brings together loans, deals, portfolios, payments, inspections, and collaboration to deliver faster execution, greater transparency, efficiency, and trust across the industry. Today, Built is a partner to more than 350 lenders, over 80,000 borrowers and owners, and thousands of contractors, powering 86,000 active projects valued at more than $300 billion. Learn more at

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 product ionizing 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.

You’ll 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 (Sage Maker, 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

This role is hands‑on and foundational. You’ll be shaping how machine learning operates at Built for years to come.

We’re looking for a builder - someone who has personally designed and productionized ML infrastructure before.

• 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 (Sage Maker preferred)
• Proven experience building infrastructure‑as‑code solutions (Terraform)
• Experience product ionizing ML workflows end‑to‑end, not just optimizing existing systems
• 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…