• Develop and maintain E2E data pipelines, backend ingestion and participate in the build of Samsara’s Data Platform to enable advanced automation and analytics. • Work with data from a variety of sources including but not limited to: CRM data, Product data, Marketing data, Order flow data, Support ticket volume data. • Manage critical data pipelines to enable our growth initiatives and advanced analytics. • Facilitate data integration and transformation requirements for moving data between applications; ensuring interoperability of applications with data layers and data lake. • Develop and improve the current data architecture, data quality, monitoring, observability and data availability. • Write data transformations in SQL/Python to generate data products consumed by customer systems and Analytics, Marketing Operations, Sales Operations teams. • Champion, role model, and embed Samsara’s cultural principles as we scale globally and across new offices.

• Build and maintain AI request handlers, generation configs, and routing logic for our third-party model inference providers • Implement monitoring systems, dashboards, and alerting for production AI system health, performance, and costs • Design metrics for analyzing the health and efficiency of our production AI resources • Create agentic automations for common AI infrastructure tasks and analytics pipelines • Manage database schemas for AI configurations and evaluation data • Work with the team to define best practices for AI system reliability and observability

• Partner Across the Organization • Work closely with Data and business teams to understand workflows, identify pain points, and find high-impact opportunities for AI-driven automation and efficiency. • Turn business problems into clear AI use cases. • Design and iterate on practical AI tools such as copilots, agents, and automated workflows using modern GenAI techniques. Write clean, reliable code in Python and Golang to support these solutions. • Partner with engineering and data teams to turn prototypes into secure, scalable, production-ready systems. • Guide teams on AI capabilities, responsible use, and best practices. Mentor others on design, coding standards, and effective AI tools. • Pilot new solutions with teams, gather feedback, iterate quickly, and support rollout to ensure real business impact. • Work with Security, Legal, and Compliance to meet privacy, security, and ethical standards. • Define success metrics and track outcomes such as time saved, cost reduction, increased speed and productivity, revenue impact, risk reduction. • Continuously explore new AI models, tools, and practices to bring practical innovations into the business.

• Define and own end-to-end AI architecture for multiple enterprise use cases, including data flows, model lifecycle, and serving patterns across Azure and AWS. • Design AI/ML solution blueprints covering data ingestion, feature stores, training pipelines, model registry, deployment, monitoring, retraining, and decommissioning. • Establish and standardize MLOps frameworks (MLflow/Kubeflow/Airflow, Docker, Kubernetes) and reference implementations that AI/ML engineers and data scientists can reuse across teams. • Collaborate with product, data, and business stakeholders to identify AI opportunities, shape solution options, and align AI architectures with business and non-functional requirements (scalability, reliability, cost, latency). • Define and enforce governance for model approval, explainability, versioning, drift monitoring, and compliance with data privacy and regulatory requirements relevant to the organization. • Guide the selection and integration of cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Azure Functions, API gateways, monitoring stacks) into cohesive AI platforms. • Work closely with security, compliance, data, and enterprise architecture teams to ensure AI solutions meet standards for security, resilience, observability, and cost optimisation. • Provide technical leadership and mentoring to AI engineers, ML engineers, and data scientists on best practices in AI architecture, MLOps, and cloud-native design. • Create and maintain architectural artefacts: high-level designs, detailed solution diagrams, standards, and documentation for AI platforms and reference solutions.