AI Agent Developer

About the role

Geotab ® is a global leader in IoT and connected transportation and certified "Great Place to Work™." We are a company of diverse and talented individuals who work together to help businesses grow and succeed, and increase the safety and sustainability of our communities. Geotab is advancing security, connecting commercial vehicles to the internet and providing web-based analytics to help customers better manage their fleets. Geotab's open platform and Geotab Marketplace ®, offering hundreds of third-party solution options, allows both small and large businesses to automate operations by integrating vehicle data with their other data assets. Processing billions of data points a day, Geotab leverages data analytics and machine learning to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety and achieve strong compliance to regulatory changes. Our team is growing and we're looking for people who follow their passion, think differently and want to make an impact. Ours is a fast paced, ever changing environment. Geotabbers accept that challenge and are willing to take on new tasks and activities - ones that may not always be described in the initial job description. Join us for a fulfilling career with opportunities to innovate, great benefits, and our fun and inclusive work culture. Reach your full potential with Geotab. To see what it's like to be a Geotabber, check out our blog and follow us @InsideGeotab on Instagram .

Responsibilities

• How you'll make an impact:
• In concert with executive AI stakeholders across the organization, develop and implement the vision, strategy, and operating model for the internal AI Agent CoE, mirroring successful aspects of the Data CoE.
• Define governance frameworks, best practices, and standards for internal AI agent development, deployment, and maintenance.
• Develop best practices for inclusion of knowledge into our agents (but the responsibility of knowledge management at Geotab is not in scope)
• Establish processes for identifying, prioritizing, and managing a portfolio of internal AI agent use cases.
• Partner closely with business units across the enterprise to understand their processes, pain points, and opportunities for AI agent application.
• Identify and qualify (with the assistance of key stakeholders) high-potential use cases where AI agents can deliver significant ROI, efficiency gains, or operational improvements.
• Prioritize initiatives based on feasibility, business impact, and strategic alignment, focusing initially on a specific area for rapid, iterative deployment to production.
• Lead the design, development (including hands-on coding/prototyping initially), testing, and deployment of pilot and production AI agent solutions.
• Collaborate extensively with the existing GenAI team to understand and potentially leverage their agentic platform, tools, and expertise for internal use cases, ensuring synergy and avoiding redundant efforts.
• Collaborate closely with platform, legal, compliance, security, and data governance teams to ensure agents adhere to all data governance, security, and regulatory guardrails.
• Serve as the primary point of contact and subject matter expert for internal AI agent capabilities.
• Build and foster an internal AI Agent developer community through knowledge sharing, workshops, and direct guidance.
• Develop and share best practices, reusable components, and documentation to empower other teams to build their own agents.
• Evangelize the potential of AI agents internally through demonstrations, workshops, and knowledge sharing; act as a change agent.
• Define key performance indicators (KPIs) and metrics to measure the success and ROI of implemented AI agents (e.g., time saved, cost reduction, process improvement).
• Monitor agent performance, gather user feedback, and drive continuous improvement cycles.
• Report on CoE progress, outcomes, and value generated to senior leadership.
• Stay abreast of the latest advancements in AI, Large Language Models (LLMs), agentic frameworks, prompt engineering, and enabling technologi

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