Senior Data and AI Platform Architect

Veralto

full-time

Posted on: 9/19/2025

Origin: • 🇺🇸 United States • Colorado

✨ AI Apply

💰 $160,000 - $180,000 per year

Senior

AirflowAzureCloudDistributed SystemsDockerKubernetesMicroservicesPostgresTerraform

About the role

Lead the design and implementation of TraceGains' next-generation data and MLOps platform on Azure
Architect end-to-end MLOps capabilities that support Intelligent Document Processing, supply chain risk prediction, and knowledge graph applications
Architect scalable, multi-tenant data platform using Azure Data Factory, Databricks, and Azure Synapse Analytics
Design hybrid data architectures supporting operational systems, AI workloads, and knowledge graphs
Build vector databases and graph database infrastructure for RAG applications and semantic search
Design and implement comprehensive MLOps platform on Azure supporting the full ML lifecycle
Build automated ML pipelines using Azure ML, MLflow, and Azure DevOps for CI/CD
Implement real-time inference infrastructure with monitoring, alerting, and automated drift detection
Build and hire a technical team of data engineers
End-to-end knowledge graph lifecycle management including hydration from taxonomies/ontologies
Implement Infrastructure as Code using Terraform and build CI/CD pipelines for data products and ML models
Design containerized microservices architecture using Docker and Azure Kubernetes Service
Create self-service capabilities with comprehensive monitoring and observability
Report to the VP of Engineering

Master's degree in Computer Science, Data Engineering, or related field (or equivalent experience)
8-12 years building enterprise data and AI platforms in production environments
Proven track record designing and implementing MLOps platforms on Azure with measurable business impact
5+ years hands-on experience with Azure ML, Azure Synapse, Azure Data Factory, and/or Azure Kubernetes Service
MLOps & AI Platforms: MLflow, Kubeflow or Azure ML pipelines, model monitoring and drift detection
Data Engineering: Modern data stack (dbt, Airflow), real-time streaming, data lake/warehouse architecture
Cloud Infrastructure: Azure native services, Terraform, Kubernetes, containerization strategies
Databases & Storage: PostgreSQL, graph databases, vector stores, distributed systems design
DevOps & Platform Engineering: CI/CD for ML, Infrastructure as Code, monitoring and observability
Proven ability to establish shared platform capabilities that serve multiple product teams
Strong communication skills with ability to present to executive leadership
Track record of cross-functional collaboration with AI product teams, ML, and business stakeholders
Experience establishing technical standards and governance frameworks across distributed teams