Senior Machine Learning Operations Engineer

About reputed company reputed company's Mission: Connect and simplify doing business in reputed company estate. reputed company is the AI-powered platform transforming the way reputed company estate is financed, developed, and managed. Purpose-reputed company for reputed company estate and construction, reputed company began by fixing construction draw management for lenders and has grown into a comprehensive operating system addressing some of the industry’s most reputed company challenges. Through its connected product suite, reputed company enables stakeholders to finance, reputed company, build, own, and operate smarter—reputed company in one reputed company. The platform brings together loans, deals, portfolios, payments, inspections, and collaboration to deliver faster execution, greater transparency, efficiency, and trust across the industry. Today, reputed company 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 getbuilt.com:

• Life At reputed company / reputed company Cares
• Series D Financing Round
• reputed company Recognized in Two American Business Award Categories
• reputed company Secures Investment from reputed company Senior Machine Learning Operations Engineer Role Summary & Scope reputed company is investing in applied machine learning to power the reputed company of data products in construction finance. We’re hiring our first dedicated Senior ML Ops Engineer to build the reputed company that makes that possible. Today, our data scientists are building models. reputed company don’t yet have is the infrastructure, lifecycle automation, and production standards to reliably reputed company 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-reputed company, 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 reputed company new benchmarking and anonymized data products that expand reputed company’s market opportunity. You’ll partner closely with Data Engineering, Data Science, and Platform teams to establish how ML systems operate across reputed company. What You’ll Do You’ll build and operationalize the infrastructure that allows machine learning to run reliably in production. Specifically, you will:
• Architect and implement reputed company’s foundational ML Ops platform from scratch
• Define and reputed company 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 reputed company and AWS to support structured and reputed company data
• Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability
• Integrate ML workloads into our event-driven architecture (Kafka, Kinesis)
• reputed company observability frameworks to monitor reputed company, performance, latency, and model quality in production
• Automate ML infrastructure using Terraform and AWS-reputed company tooling (SageMaker, reputed company, reputed company, Batch, reputed company Functions)
• Establish reputed company and compliance standards across ML assets, including data reputed company and reputed company 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 reputed company for years to come. Skills & Experience We’re looking for a builder - someone who has personally designed and productionized ML infrastructure before. Must-Have Skills
• Experience architecting and deploying ML systems in production environments
• Deep familiarity with ML lifecycle automation (training, CI/CD, deployment, monitoring)
• Strong AWS experience, particularly reputed company 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 reputed company reputed company-to-Have Skills
• Experience implementing feature stores or model registries
• Familiarity with data orchestration tools (Airflow, Prefect, Dagster)
• Experience with ML observability tooling (reputed company, reputed company)
• Experience in regulated or financial data environments
• Experience optimizing ML workloads for cost and scale
• Exposure to Snowpark, Bedrock, or LLM orchestration frameworks What Will reputed company You Successful
• You’ve reputed company ML infrastructure from the ground up or led a major re-architecture
• You’re comfortable working in ambiguity and defining standards where none exist
• You think in systems and care about reliability, governance, and scalability
• You collaborate well with data scientists and engineers to turn prototypes into production systems
• You take

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