AI Solutions Developer

GEOTAB

. Architect, code, deploy, and maintain custom AI agents and automation solutions for Global Enablement .

Posted 10/20/2026full-timeAtlanta • Nevada • United StatesMid-LevelSenior💰 $83,200 - $102,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and deploying AI solutions, including backend logic, microservices, and secure API integrations. Proficient in building scalable AI workflows and implementing automation strategies while ensuring security and compliance.

Highest-signal resume keywords

Python ProficiencyAI/ML ExperienceRAG Pipeline DesignGoogle Cloud DeploymentRESTful API Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonJavaScriptTypeScriptLangChainAutoGenCrewAIPostgreSQLPgvectorCI/CDSecure Coding Practices

Soft Skills

CollaborationProblem-SolvingCommunication

Tools & Technologies

Google CloudVertex AICloud FunctionsOpenAI APIGemini APIAnthropic APIPineconeWeaviateFAISS

Certifications & Qualifications

Google Cloud CertificationAzure CertificationAWS Certification

Industry Keywords

AI AgentsAutomation SolutionsConversational AISpeech RecognitionMultilingual CapabilitiesMicroservicesAgentic WorkflowsContinuous ImprovementTechnical SpecificationsOperational Handover

Tech Stack

Tools & technologies

AWSAzureCloudJavaScriptMicroservicesPandasPostgresPythonTypeScript

About the role

Key responsibilities & impact

Architect, code, deploy, and maintain custom AI agents and automation solutions for Global Enablement
Develop backend logic, microservices, and scalable AI workflows
Build retrieval augmented generation (RAG) systems integrating LLMs with PostgreSQL/pgvector and vector databases
Develop full-stack AI applications with intuitive interfaces and secure integrations
Implement conversation management, context retention, structured outputs, and tool calling
Evaluate and integrate conversational AI, speech recognition, speech generation, interactive avatars, and multilingual capabilities
Build self-service tools and AI-supported workflows with subject matter experts
Design secure RESTful API integrations with business, learning, sales enablement, knowledge, and third-party platforms
Develop reusable libraries, API wrappers, and solution frameworks
Collaborate with IT, GenAI platform teams, subject matter experts, and end users
Implement authentication, authorization, role-based access controls, security safeguards, and responsible AI guardrails
Establish CI/CD pipelines and AI evaluation processes
Monitor latency, token usage, error rates, cost efficiency, and response quality
Maintain deployed solutions through monitoring, troubleshooting, updates, and continuous improvement
Document deployment, support, and operational handover procedures
Translate business requirements into technical specifications and architecture designs
Conduct technical feasibility assessments and prototype emerging AI technologies
Define project scopes, delivery phases, dependencies, success criteria, risks, and tradeoffs
Identify automation opportunities and measure impact through adoption, time saved, quality, operating costs, and enablement outcomes

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related quantitative field; Master’s degree preferred
3–5 years of proven experience in AI/ML, Data Science, or software engineering roles 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 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
Solid knowledge of Git, testing, CI/CD, RESTful APIs, and secure coding practices
Professional certifications in Google Cloud, Azure, AWS, or specific AI technologies are considered an asset
Experience with speech, avatar, or multilingual capabilities is considered an asset
Eligibility to work in the United States

Benefits

Comp & perks

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