Senior Data & AI Platform Architect

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Veralto

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B2B • Energy • Science

Veralto is a global enterprise comprising 13 operating companies and over 300 locations worldwide. With a workforce of 16,000 associates, Veralto focuses on impactful work in areas crucial to everyday life, such as water, food, and medicine. The company's Water Quality division manages, treats, purifies, and protects water on a global scale, while the Product Quality & Innovation division ensures the safety and authenticity of essential goods in the global supply chain. Committed to fostering a diverse and inclusive workplace, Veralto invests in its employees' growth through hands-on learning and career development opportunities, supported by a global network and the resources of an S&P 500 company.

10,000+ employees

🤝 B2B

⚡ Energy

🔬 Science

📋 Description

• lead the design and implementation of TraceGains' next-generation data and MLOps platform on Azure • architect end-to-end MLOps capabilities • Build 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 • 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

🎯 Requirements

• 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

🏖️ Benefits

• paid time off • medical/dental/vision insurance • 401(k)