AI Agent Developer

Geotab•Oakville, ON

8h•CA$104,400 - CA$130,000•Hybrid

About The Position

Geotab is seeking an AI Agent Developer to drive the internal AI Agent Centre of Excellence (CoE). This role focuses on identifying, developing, and deploying AI agents to address internal business challenges and deliver measurable ROI. The ideal candidate is passionate about technology and eager to join an industry leader in a fast-paced, evolving environment.

Requirements

5+ years of proven experience in AI/ML, Data Science, or software engineering roles with a focus on building and deploying intelligent systems or automation.
Demonstrated experience with Generative AI concepts, LLMs (e.g., GPT series, Claude, Llama), and related technologies (e.g., vector databases, embedding models, prompt engineering).
Hands-on experience developing AI-driven applications, automations, or prototypes.
Specific experience building or working with AI agents or agentic frameworks (e.g., LangChain, CrewAI, AutoGen, Microsoft Copilot Studio/Frameworks) is highly desirable.
Strong ability to identify business problems/opportunities and translate them into tangible technical solutions.
Proven ability to lead initiatives from concept to production, manage projects, and influence stakeholders in a corporate environment.
Experience working collaboratively across technical and non-technical teams, including infrastructure and operations teams.
Proficiency in Python and relevant AI/ML libraries (e.g., scikit-learn, pandas, libraries for interacting with LLMs).
Experience interacting with LLM APIs and understanding their capabilities, limitations, and cost implications.
Solid understanding of software development best practices (e.g., version control with Git, testing, CI/CD concepts).
Familiarity with data integration patterns, APIs (RESTful), and core cloud infrastructure concepts and services (AWS, Azure, or GCP).
Excellent communication (written and verbal), presentation, and interpersonal skills – ability to explain complex concepts to diverse audiences.
Strong analytical and problem-solving abilities.
Strategic thinking combined with a pragmatic, results-oriented, "get-it-done" approach.
Ability to operate independently, manage ambiguity, and drive initiatives forward.
Passion for AI and its potential to transform business operations internally.
Strong business acumen.

Nice To Haves

Experience operating in or establishing a CoE structure is a plus.

Responsibilities

Driving the internal AI Agent Centre of Excellence (CoE) at Geotab.
Accelerating the adoption of AI agents internally through high-impact projects.
Collaborating closely with the existing Generative AI product team, with an emphasis on internal applications.
Developing and implementing the vision, strategy, and operating model for the internal AI Agent CoE.
Defining governance frameworks, best practices, and standards for internal AI agent development, deployment, and maintenance.
Developing best practices for inclusion of knowledge into our agents.
Establishing processes for identifying, prioritizing, and managing a portfolio of internal AI agent use cases.
Partnering closely with business units across the enterprise to understand their processes, pain points, and opportunities for AI agent application.
Identifying and qualifying high-potential use cases where AI agents can deliver significant ROI, efficiency gains, or operational improvements.
Prioritizing initiatives based on feasibility, business impact, and strategic alignment.
Leading the design, development (including hands-on coding/prototyping initially), testing, and deployment of pilot and production AI agent solutions.
Collaborating extensively with the existing GenAI team to understand and potentially leverage their agentic platform, tools, and expertise.
Collaborating closely with platform, legal, compliance, security, and data governance teams to ensure agents adhere to all data governance, security, and regulatory guardrails.
Serving as the primary point of contact and subject matter expert for internal AI agent capabilities.
Building and fostering an internal AI Agent developer community through knowledge sharing, workshops, and direct guidance.
Developing and sharing best practices, reusable components, and documentation to empower other teams to build their own agents.
Evangelizing the potential of AI agents internally through demonstrations, workshops, and knowledge sharing; acting as a change agent.
Defining key performance indicators (KPIs) and metrics to measure the success and ROI of implemented AI agents.
Monitoring agent performance, gathering user feedback, and driving continuous improvement cycles.
Reporting on CoE progress, outcomes, and value generated to senior leadership.
Staying abreast of the latest advancements in AI, Large Language Models (LLMs), agentic frameworks, prompt engineering, and enabling technologies.
Evaluating and recommending appropriate cloud tools, platforms, and services to support AI agent development and deployment.
Evaluating new tools and techniques for potential application within the enterprise context.