Senior Data & AI Platform Architect

Veralto Global-posted 3 months ago

$160,000 - $180,000/Yr

Full-time • Senior

Denver, CO

501-1,000 employees

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We're seeking a Senior Data & AI Platform Architect to lead the design and implementation of TraceGains' next-generation data and MLOps platform on Azure. You'll be the technical visionary who transforms our AI strategy into scalable, production-ready infrastructure that powers intelligent supply chain solutions while maintaining our rigorous standards for customer data privacy, integrity, and transparency. Reporting to the VP of Engineering, you'll architect end-to-end MLOps capabilities that support everything from our proven Intelligent Document Processing solution to advanced supply chain risk prediction and knowledge graph applications. This role combines deep technical expertise with strategic thinking—you'll build self-service platforms that enable data scientists and engineers to innovate rapidly while ensuring enterprise-grade reliability and compliance.

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 from experimentation to production
Build automated ML pipelines using Azure ML, MLflow, and Azure DevOps for continuous integration and deployment
Implement real-time inference infrastructure with monitoring, alerting, and automated drift detection
Build a technical team of data engineers
End-to-end lifecycle management including hydration from existing taxonomies/ontologies, leveraging TopBraid EDG experience
High-performance graph query services and APIs for real-time access to supply chain relationships
Automated validation, conflict resolution, and data quality monitoring to ensure graph consistency and accuracy
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

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
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

Experience building and mentoring technical teams (data engineers, AI/ML engineers, platform engineers)
Experience with supply chain, food safety, or regulatory compliance domains
Multi-cloud architecture experience with Azure as primary and AWS/GCP familiarity
Knowledge of LLMs, RAG architectures, and advanced NLP applications
Open source contributions to ML or data platform tools
Experience with knowledge graphs and ontology management
Background in privacy-preserving ML techniques and federated learning
Experience building vector databases and graph database infrastructure for RAG applications and semantic search

Paid time off
Medical/dental/vision insurance
401(k) to eligible employees
Bonus Pay

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