TraceGains
Overview
Senior Data & AI Platform Architect role at TraceGains. Lead the design and implementation of TraceGains\' next-generation data and MLOps platform on Azure. Transform AI strategy into scalable, production-ready infrastructure that powers intelligent supply chain solutions while maintaining customer data privacy, integrity, and transparency. Reporting to the VP of Engineering, you\'ll architect end-to-end MLOps capabilities that support solutions from Intelligent Document Processing to advanced supply chain risk prediction and knowledge graph applications. Build self-service platforms that enable data scientists and engineers to innovate rapidly while ensuring enterprise-grade reliability and compliance. Responsibilities
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 a 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 and lead a technical team of data engineers Manage end-to-end lifecycle of knowledge graphs, including hydration from existing taxonomies/ontologies and real-time APIs Establish Infrastructure as Code and CI/CD pipelines for data products and ML models Design containerized microservices using Docker and Azure Kubernetes Service Enable self-service capabilities with comprehensive monitoring and observability Define success metrics and drive delivery for year 1 goals (e.g., reduce deployment time, scale AI workloads, enable self-serve capabilities) Qualifications
Required Qualifications
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 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 Technical Expertise
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 Leadership & Collaboration
Establish shared platform capabilities for multiple product teams Strong communication skills with ability to present to executive leadership Cross-functional collaboration with AI product teams, ML, and business stakeholders Experience establishing technical standards and governance across distributed teams Preferred Qualifications
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 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 Privacy-preserving ML techniques and federated learning Experience building vector databases and graph database infrastructure for RAG and semantic search Compensation & Benefits
The compensation range for this role is $160,000 - $180,000 USD per year. This role is eligible for bonus pay. We offer a comprehensive benefits package including paid time off, medical/dental/vision insurance, and 401(k). This description may be updated; final compensation will be determined based on factors including education, experience, location, and company needs. EEO & Accessibility
Veralto Corporation and all Veralto companies are committed to equal opportunity regardless of race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. Reasonable accommodations are available for applicants with disabilities. If you need accommodation during the application process, contact applyassistance@veralto.com.
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Senior Data & AI Platform Architect role at TraceGains. Lead the design and implementation of TraceGains\' next-generation data and MLOps platform on Azure. Transform AI strategy into scalable, production-ready infrastructure that powers intelligent supply chain solutions while maintaining customer data privacy, integrity, and transparency. Reporting to the VP of Engineering, you\'ll architect end-to-end MLOps capabilities that support solutions from Intelligent Document Processing to advanced supply chain risk prediction and knowledge graph applications. Build self-service platforms that enable data scientists and engineers to innovate rapidly while ensuring enterprise-grade reliability and compliance. Responsibilities
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 a 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 and lead a technical team of data engineers Manage end-to-end lifecycle of knowledge graphs, including hydration from existing taxonomies/ontologies and real-time APIs Establish Infrastructure as Code and CI/CD pipelines for data products and ML models Design containerized microservices using Docker and Azure Kubernetes Service Enable self-service capabilities with comprehensive monitoring and observability Define success metrics and drive delivery for year 1 goals (e.g., reduce deployment time, scale AI workloads, enable self-serve capabilities) Qualifications
Required Qualifications
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 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 Technical Expertise
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 Leadership & Collaboration
Establish shared platform capabilities for multiple product teams Strong communication skills with ability to present to executive leadership Cross-functional collaboration with AI product teams, ML, and business stakeholders Experience establishing technical standards and governance across distributed teams Preferred Qualifications
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 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 Privacy-preserving ML techniques and federated learning Experience building vector databases and graph database infrastructure for RAG and semantic search Compensation & Benefits
The compensation range for this role is $160,000 - $180,000 USD per year. This role is eligible for bonus pay. We offer a comprehensive benefits package including paid time off, medical/dental/vision insurance, and 401(k). This description may be updated; final compensation will be determined based on factors including education, experience, location, and company needs. EEO & Accessibility
Veralto Corporation and all Veralto companies are committed to equal opportunity regardless of race, color, national origin, religion, sex, age, marital status, disability, veteran status, sexual orientation, gender identity, or other characteristics protected by law. Reasonable accommodations are available for applicants with disabilities. If you need accommodation during the application process, contact applyassistance@veralto.com.
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