AI Solutions Developer
Who we are:
Who you are:
We are always looking for amazing talent who can contribute to our growth and deliver results! Geotab is seeking a AI Solutions Developer who will be responsible for architecting, building, and operationalizing custom AI agents, copilots, and automated workflows that solve real business problems and deliver measurable ROI. If you love technology, and are keen to join an industry leader — we would love to hear from you!
What you'll do:
As an AI Solutions Developer, you will architect, build, and deploy custom AI agents and automation from the ground up. You’ll leverage Python and frameworks like LangChain or AutoGen to solve complex business problems, translating high-level needs from Legal, Compliance, and L&D into robust technical specifications and architectural designs.
How you'll make an impact:
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Accountable for the strategic direction of HR support for designated functional group while ensuring strategic direction aligns with overall organizational strategy.
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Architect, code, and deploy custom AI agents and automation solutions from scratch using Python and relevant frameworks (e.g., LangChain, AutoGen) to solve complex business challenges.
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Develop and maintain backend logic and microservices required to support AI workflows, ensuring high availability and scalability of deployed agents.
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Build custom retrieval pipelines (RAG) by integrating Large Language Models (LLMs) with vector databases (e.g., Pinecone, Weaviate) to enable secure interaction with internal proprietary data.
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Write clean, maintainable, and efficient code for content generation, localization, and multimedia processing automations, adhering to software development best practices (version control, code reviews, testing).
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Design and implement secure API integrations (RESTful) to connect AI agents with enterprise systems, Legal/Compliance databases, and third-party platforms.
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Develop reusable code libraries, API wrappers, and solution frameworks to accelerate technical development across the division and ensure consistency in codebase.
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Collaborate with IT and GenAI platform teams to ensure local solutions align with enterprise infrastructure, leveraging cloud resources (Google Vertex AI, Azure, AWS) effectively.
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Implement code-level security measures to ensure agents handling sensitive information (e.g., contracts, internal documents) strictly adhere to data privacy and responsible AI guardrails.
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Establish CI/CD pipelines for the testing and deployment of AI agents, ensuring rigorous quality control and risk mitigation before production release.
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Monitor technical performance metrics (latency, token usage, error rates) and optimize code to ensure cost-efficiency and response quality.
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Translate complex business requirements from Legal, Compliance, and L&D stakeholders into detailed technical specifications and architecture designs.
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Conduct technical feasibility assessments on proposed use cases to determine if they require custom code solutions versus off-the-shelf configuration.
What you'll bring to the role:
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Strong proficiency in Python for application development, including experience with data manipulation libraries (e.g., pandas) and asynchronous programming patterns. Familiarity with JavaScript/TypeScript or server-side scripting is a strong asset.
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Hands-on experience building custom AI agents using code-first frameworks such as LangChain, AutoGen, or CrewAI, moving beyond simple prompt engineering to architect complex, multi-step agentic workflows.
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Deep technical understanding of interacting with LLM APIs (OpenAI, Gemini, Anthropic), including managing context windows, token optimization, structured output parsing, and function calling.
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Demonstrated experience designing data retrieval pipelines (RAG); proficiency working with vector databases (e.g., Pinecone, Weaviate, FAISS) and understanding embedding models for semantic search.
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Experience deploying applications within cloud environments, specifically the Google Cloud ecosystem (Vertex AI, Cloud Functions). Knowledge of serverless architecture and cloud security best practices.
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Solid understanding of modern software engineering workflows, including version control (Git/GitHub), rigorous testing methodologies (unit/integration testing), and CI/CD concepts for automated deployment.
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Strong ability to design, document, and consume RESTful APIs to integrate AI agents with internal business systems and third-party services securely.
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Fundamental understanding of secure coding practices, particularly regarding data privacy, encryption, and handling sensitive information within AI/ML applications.
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Ability to translate loose business requirements from non-technical stakeholders (Legal, L&D) into precise technical specifications and architecture plans.
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Strong problem-solving skills with the ability to debug complex code issues independently and research technical solutions in a rapidly evolving AI landscape.
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Discipline in creating technical documentation (code comments, API docs, architecture diagrams) to ensure maintainability and scalability of developed solutions.
Why job seekers choose Geotab:
Flex working arrangements
Home office reimbursement program
Baby bonus & parental leave top up program
Online learning and networking opportunities
Electric vehicle purchase incentive program
Competitive medical and dental benefits
Retirement savings program
*The above are offered to full-time permanent employees only
How we work:
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