AI Solutions Developer (Remote)

We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking an AI Solutions Developer who will be responsible for architecting building and operationalizing custom AI applications agents self-service tools and automated workflows that help our Global Enablement teams work smarter support revenue generation and deliver measurable ROI. This is a hands-on engineering role for a builder who loves turning ideas into secure reliable production-ready solutions. If you love building with AI and are keen to join an industry leader we would love to hear from you!

As an AI Solutions Developer your key area of responsibility will be designing developing deploying and maintaining AI-powered applications that strengthen sales enablement learning design customer education and operational efficiency across Global Enablement. You will be responsible for coding custom AI agents and automation solutions in Python building retrieval augmented generation (RAG) systems developing full-stack applications with intuitive user interfaces and integrating AI with business systems through secure APIs. You could be building an AI trainer that supports learning and skill development a sales enablement engine that curates the right content pitch decks talk tracks and positioning for each sellers needs or automated workflows that create and update training videos and user guides as our products evolve. You will also need to establish engineering best practices CI/CD pipelines AI evaluation processes and security guardrails while monitoring performance and cost to keep solutions reliable and efficient. You will need to work closely with subject matter experts across enablement functions to translate specialized knowledge and learning objectives into practical applications and partner with IT and GenAI platform teams to ensure your solutions align with company infrastructure and technology standards.

To be successful in this role you will be an independent problem solver and a strong communicator who can explain technical capabilities limitations risks and tradeoffs clearly to nontechnical stakeholders. You take ownership of your work from initial concept through deployment and ongoing operation and you balance technical capability with usability and measurable business addition the successful candidate will have the ability to translate loosely defined business requirements into precise technical specifications along with a disciplined approach to documentation and a passion for staying current in a rapidly evolving AI landscape.

• Architect code and deploy custom AI agents and automation solutions using Python and relevant frameworks such as LangChain and AutoGen to solve complex business challenges within Global Enablement.

• Develop and maintain backend logic and microservices required to support AI workflows ensuring high availability and scalability of deployed agents.

• Build custom retrieval augmented generation (RAG) systems by integrating large language models (LLMs) with PostgreSQL with pgvector and vector databases such as Pinecone and Weaviate enabling secure interaction with proprietary data and approved content.

• Write clean maintainable and efficient code for content generation localization and multimedia processing automations adhering to software development best practices including version control code reviews and testing.

• Develop complete AI applications including intuitive user interfaces backend services and secure integrations for internal and external users.

• Implement conversation management context retention structured outputs and tool calling to support interactive learning guidance and multi step workflows.

• Evaluate and integrate conversational AI speech recognition speech generation interactive avatars and multilingual capabilities.

• Build self service tools and AI supported workflows in partnership with subject matter experts incorporating appropriate review and approval steps.

• Design and implement secure RESTful API integrations to connect AI agents with business systems learning and sales enablement platforms knowledge repositories and third party platforms.

• Develop reusable code libraries API wrappers and solution frameworks to accelerate technical development across Global Enablement and ensure consistency in the codebase.

• Collaborate with IT and GenAI platform teams to ensure departmental solutions align with company infrastructure and technology standards leveraging cloud resources such as Google Vertex AI Azure and AWS effectively.

• Develop knowledge ingestion and synchronization processes to keep connected content current approved and appropriate for its intended audience.

• Implement authentication authorization and role based access controls for internal partner and customer facing applications.

• Design solutions that support future integrations and additional enablement use cases without unnecessary duplication or redevelopment.

• Implement code level security measures to ensure agents handling sensitive information including internal documents and customer data adhere to data privacy and responsible AI guardrails.

• Establish continuous integration and continuous deployment (CI/CD) pipelines for testing and deploying AI agents ensuring rigorous quality control and risk mitigation before production release.

• Monitor technical performance metrics including latency token usage and error rates and optimize code to ensure cost efficiency and response quality.

• Establish AI evaluation processes to test answer accuracy retrieval quality relevance and appropriate handling of unsupported requests.

• Implement safeguards against unauthorized information access prompt injection and unintended actions including human approval and escalation where appropriate.

• Maintain separation of internal partner and customer facing content with appropriate controls to protect information across users and organizations.

• Maintain deployed solutions through monitoring troubleshooting updates and continuous improvement.

• Document deployment procedures support requirements and operational handover processes to ensure continuity.

• Translate complex business requirements from Global Enablement stakeholders into detailed technical specifications and architecture designs supporting revenue generation sales readiness training effectiveness and operational efficiency.

• Conduct technical feasibility assessments on proposed use cases to determine whether they require custom code existing platform configuration or a combination of approaches.

• Research and prototype emerging AI technologies including new LLM releases and agentic frameworks to maintain an effective and current technical stack for Global Enablement.

• Partner with subject matter experts to translate specialized knowledge learning objectives and business processes into AI applications.

• Collaborate with experts and end users to validate accuracy instructional quality usability and effectiveness.

• Define clear scopes delivery phases dependencies and success criteria communicating technical risks and tradeoffs to stakeholders.

• Identify opportunities to automate repetitive work and create self service experiences across enablement teams selecting AI or conventional automation as appropriate.

• Evaluate opportunities to consolidate or replace existing applications with AI enabled solutions considering functionality cost security migration requirements and ongoing support.

• Measure solution impact through adoption time saved quality improvements operating costs and contribution to agreed enablement outcomes.

• Bachelors degree in Computer Science Data Science Engineering Artificial Intelligence or a related quantitative field; Masters degree preferred.

• 35 years of proven experience in AI/ML Data Science or software engineering roles with a focus on building and deploying intelligent systems or automation.

• Strong proficiency in Python (including pandas and asynchronous programming) and working proficiency in JavaScript or TypeScript for user-facing applications.

• Hands-on experience building multi-step agentic workflows with code-first frameworks such as LangChain AutoGen or CrewAI and integrating LLM APIs such as OpenAI Gemini and Anthropic.

• Demonstrated experience designing RAG pipelines using PostgreSQL with pgvector and vector databases or search libraries such as Pinecone Weaviate and FAISS.

• Experience deploying applications in Google Cloud (Vertex AI Cloud Functions) with solid knowledge of Git testing CI/CD RESTful APIs and secure coding practices.

• Professional certifications in Google Cloud Azure AWS or specific AI technologies and experience with speech avatar or multilingual capabilities are considered an asset.