AI Solutions Developer (Remote)

Geotab • Las Vegas, GA

1d

About The Position

Geotab is seeking an AI Solutions Developer to architect, build, deploy, and operate custom AI applications, agents, self-service tools, and automated workflows for its Global Enablement teams. The role focuses on helping teams work more efficiently, supporting revenue generation, and delivering measurable business value. It is a hands-on engineering position involving production-ready solutions that are secure, reliable, scalable, and usable.

The developer will create Python-based AI agents and automation, retrieval-augmented generation (RAG) systems, full-stack applications, and secure integrations with business systems. Potential solutions include AI trainers for learning and skill development, sales enablement tools that curate relevant content and guidance, and workflows that create or update training materials, videos, and user guides. The role also establishes engineering practices, CI/CD pipelines, evaluation processes, and security controls, while monitoring system performance, response quality, and cost.

The developer partners with Global Enablement subject matter experts, IT, and GenAI platform teams to turn business needs and specialized knowledge into practical applications aligned with company standards. The position calls for ownership from concept through ongoing operation, clear communication of technical risks and tradeoffs, careful documentation, and attention to usability and measurable outcomes. Geotab describes its work model as flexible hybrid; the specific workplace and attendance expectations for this position will be shared during hiring. The posting is headed “Remote,” but does not specify a definitive workplace type for this role.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related quantitative field.
• 3–5 years of experience in AI/ML, Data Science, or software engineering, focused on building and deploying intelligent systems or automation.
• Strong proficiency in Python, including pandas and asynchronous programming.
• 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.
• Experience 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, including Vertex AI and Cloud Functions.
• Knowledge of Git, testing, CI/CD, RESTful APIs, and secure coding practices.
• Ability to translate loosely defined business requirements into precise technical specifications.
• Ability to work independently, take ownership through deployment and ongoing operations, and communicate technical capabilities, limitations, risks, and tradeoffs to nontechnical stakeholders.
• Disciplined documentation practices and interest in keeping current with developments in AI.

Nice To Haves

• Master’s degree.
• Professional certifications in Google Cloud, Azure, AWS, or specific AI technologies.
• Experience with speech, avatar, or multilingual capabilities.

Responsibilities

• Architect, code, deploy, and maintain custom AI agents and automation solutions for Global Enablement, using Python and frameworks such as LangChain or AutoGen.
• Develop backend logic and microservices for high-availability, scalable AI workflows.
• Build RAG systems that connect LLMs to PostgreSQL with pgvector and vector databases or search tools such as Pinecone, Weaviate, and FAISS.
• Write maintainable code for content generation, localization, and multimedia-processing automation, following version control, review, and testing practices.
• Develop complete AI applications with user interfaces, backend services, and secure integrations for internal and external users.
• Implement conversation management, context retention, structured outputs, and tool calling for interactive and multi-step workflows.
• Evaluate and integrate conversational AI, speech recognition and generation, interactive avatars, and multilingual capabilities.
• Build self-service tools and AI-supported workflows with subject matter experts, incorporating appropriate review and approval steps.
• Design secure REST API integrations with business systems, learning and sales enablement platforms, knowledge repositories, and third-party platforms.
• Develop reusable libraries, API wrappers, and frameworks to accelerate development and maintain codebase consistency.
• Work with IT and GenAI platform teams to align solutions with company infrastructure and standards, using cloud resources such as Google Vertex AI, Azure, and AWS.
• Develop knowledge ingestion and synchronization processes to keep connected content current, approved, and appropriate for its audience.
• Implement authentication, authorization, and role-based access controls for internal, partner, and customer-facing applications.
• Design extensible solutions that support future integrations and enablement use cases without unnecessary duplication.
• Apply code-level security measures and responsible AI and privacy guardrails when handling internal documents and customer data.
• Establish CI/CD pipelines and quality controls for testing and deploying AI agents before production release.
• Monitor latency, token usage, error rates, cost efficiency, and response quality; troubleshoot and improve deployed solutions.
• Establish evaluation processes for answer accuracy, retrieval quality, relevance, and appropriate handling of unsupported requests.
• Implement safeguards against unauthorized access, prompt injection, and unintended actions, including human approval and escalation when appropriate.
• Maintain appropriate separation and access controls for internal, partner, and customer-facing content.
• Document deployment procedures, support requirements, and operational handovers.
• Translate stakeholder requirements into technical specifications and architecture designs, and communicate scopes, phases, dependencies, success criteria, risks, and tradeoffs.
• Assess technical feasibility and determine whether use cases call for custom code, platform configuration, or a combination.
• Research and prototype emerging AI technologies, LLM releases, and agent frameworks to maintain an effective technical stack.
• Partner with subject matter experts and end users to validate accuracy, instructional quality, usability, and effectiveness.
• Identify repetitive work suitable for automation and self-service, choosing AI or conventional automation as appropriate.
• Evaluate whether existing applications could be consolidated or replaced with AI-enabled solutions, considering functionality, cost, security, migration, and support.
• Measure solution impact through adoption, time saved, quality improvements, operating costs, and contribution to enablement outcomes.

Benefits

• Flexible working arrangements.
• Home office reimbursement program.
• Baby bonus and parental leave top-up program.
• Online learning and networking opportunities.
• Electric vehicle purchase incentive program.
• Competitive medical and dental benefits.
• Retirement savings program.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level