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Senior Data & AI Platform Architect 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 thinkingyou'll build self-service platforms that enable data scientists and engineers to innovate rapidly while ensuring enterprise-grade reliability and compliance. Key Responsibilities Data Platform & Infrastructure 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 AI & MLOps Platform Architecture Design and implement comprehensive MLOps platform on Azure supporting the full ML lifecycle from experimentation to production Build automated ML pipelines using Azure ML, MLfl ow, 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 Knowledge Graph Operations & Management 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 Platform Engineering & DevOps 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 Success Metrics (Year 1) Reduce model deployment time from weeks to days Build scalable infrastructure supporting 10x growth in AI workloads Enable data scientists to self-serve 80% of their platform needs Successfully hire and onboard 2-3 senior data engineers and AI/ML engineers Required Qualifications Experience & Background 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 Technical Expertise MLOps & AI Platforms: MLfl ow, Kubefl ow or Azure ML pipelines, model monitoring and drift detection Data Engineering: Modern data stack (dbt, Airfl ow), 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 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 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 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
