Head of AI

📋 Description

• • Lead the strategic vision, architecture, design, development, and ongoing evolution of TraceGains’ next-generation Agentic AI data platform, reporting directly to the Senior Vice President of Engineering.
• • Drive the creation of a foundational data and MLOps infrastructure that will accelerate AI initiatives and scale capabilities across the organization, transforming how the global food and beverage supply chain operates.
• • Architect a scalable, multi-tenant agentic AI data platform, leveraging the Azure technology stack, to support advanced AI workloads and knowledge graph integration.
• • Design a hybrid data architecture that seamlessly integrates operational systems, agentic AI workloads, and a comprehensive knowledge graph, enabling sophisticated data utilization.
• • Build and manage infrastructure utilizing vector and graph databases, specifically tailored for Retrieval Augmented Generation (RAG) applications and advanced semantic search capabilities.
• • Design and implement a robust MLOps platform on Azure, encompassing deployment management, AI security and safety protocols, and comprehensive observability to manage the full ML lifecycle from experimentation to production.
• • Develop and maintain automated pipelines using Azure technologies for continuous integration and deployment (CI/CD), ensuring efficient and reliable model deployment.
• • Implement real-time inference infrastructure equipped with advanced monitoring, alerting, and automated drift detection mechanisms to ensure optimal performance and reliability.
• • Oversee the end-to-end lifecycle management of AI models and data, including hydration from existing taxonomies and ontologies to ensure data consistency and relevance.
• • Develop high-performance graph query services and APIs that provide real-time access to complex supply chain relationships, facilitating dynamic data analysis.
• • Deploy automated validation, conflict resolution, and data quality monitoring systems to guarantee the consistency, accuracy, and integrity of the knowledge graph.
• • Implement Infrastructure as Code (IaC) practices and build CI/CD pipelines for both data products and machine learning models, promoting automation and reproducibility.
• • Set and rigorously enforce platform standards for cost control, model/runtime selection, and performance targets, including detailed budgeting, attribution, and optimization strategies for both training and inference.
• • Lead AI governance initiatives in close collaboration with Security, Legal, and Privacy teams, translating high-level policies into practical technical controls such as access management, auditability, guardrails, retention policies, and risk management frameworks.
• • Build scalable data and evaluation systems, including the generation of synthetic datasets where appropriate, and establish automated benchmarks and release quality gates for both RAG/agents and classical ML models.
• • Create self-service capabilities for data science and engineering teams, supported by comprehensive monitoring and observability tools, to foster wider AI adoption and innovation.
• • Drive significant improvements in engineering delivery through the application of AI-based code analysis, enhancing code quality, maintainability, and overall code health.
• • Foster engineering efficiency by promoting best practices and encouraging the adoption of AI-assisted coding tools and capabilities across development teams.
• • Stay abreast of the latest research and cutting-edge AI technologies, integrating novel approaches into the platform strategy.
• • Democratize AI across TraceGains teams, enabling broader adoption and deployment of AI capabilities throughout the platform and product offerings.
• • Build and lead a high-performing platform team comprising both data engineers and AI/ML engineers, fostering a culture of innovation and technical excellence.
• • Establish TraceGains as a recognized leader in AI-driven and agentic supply chain intelligence through strategic platform development and thought leadership.
• • Make sound, budget-conscious platform decisions, meticulously balancing accuracy, latency, reliability, and total cost of ownership as the platform scales.
• • Translate technically-grounded AI governance and policy discussions (covering privacy, security, risk, and compliance) into practical engineering standards and enforceable controls.
• • Establish robust data and evaluation foundations, building scalable dataset pipelines and repeatable evaluation/benchmarking processes for generative AI and classical ML.