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About This Role
Basis:
Permanent Full Time
Area of Interest:
Software Development
Location:
Atlanta, Georgia - USA; Las Vegas, Nevada
Who we are:
Geotab ® is a global leader in IoT and connected transportation and certified “Great Place to Work™.” We are a company of diverse and talented individuals who work together to help businesses grow and succeed, and increase the safety and sustainability of our communities. Geotab is advancing security, connecting commercial vehicles to the internet and providing web-based analytics to help customers better manage their fleets. Geotab’s open platform and Geotab Marketplace ®, offering hundreds of third-party solution options, allows both small and large businesses to automate operations by integrating vehicle data with their other data assets. Processing billions of data points a day, Geotab leverages data analytics and machine learning to improve productivity, optimize fleets through the reduction of fuel consumption, enhance driver safety and achieve strong compliance to regulatory changes. Our team is growing and we’re looking for people who follow their passion, think differently and want to make an impact. Ours is a fast paced, ever changing environment. Geotabbers accept that challenge and are willing to take on new tasks and activities - ones that may not always be described in the initial job description. Join us for a fulfilling career with opportunities to innovate, great benefits, and our fun and inclusive work culture. Reach your full potential with Geotab. To see what it’s like to be a Geotabber, check out our blog and follow us @InsideGeotab on Instagram. Who you are:
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!
What you’ll do:
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 seller’s 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 value. In 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.
How you’ll make an impact:
- 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.
What you’ll bring to this role:
- 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 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.
If you got this far, we hope you're feeling excited about this role! Even if you don't feel you meet every single requirement, we still encourage you to apply. Please note: Geotab does not accept agency resumes and is not responsible for any fees related to unsolicited resumes. Please do not forward resumes to Geotab employees. 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:
At Geotab, we thrive on a flexible hybrid work model that combines the dynamic energy and connection of in-person collaboration with the independence and flexibility of virtual work. We believe that coming together in person is fundamental to cultivating trust, nurturing a vibrant culture of belonging, and fostering meaningful innovation across our diverse teams. Office visit schedules are tailored thoughtfully based on role requirements. Specific expectations regarding workplace location and on-site attendance for this position will be shared during the hiring process. Geotab verifies candidates' eligibility to work in the United States through E-Verify, an internet-based system operated by U.S. Citizen and Immigration Services. Other employment statements:
Geotab will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
We are committed to accommodating people with disabilities during the recruitment and assessment processes and when people are hired. We will ensure the accessibility needs of employees with disabilities are taken into account as part of performance management, career development, training and redeployment processes. If you require accommodation at any stage of the application process or want more information about our diversity and inclusion as well as accommodation policies and practices, please contact us at [email protected]. Geotab provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Geotab complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Geotab expressly prohibits any form of workplace harassment or discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of Geotab's employees to perform their job duties may result in discipline up to and including discharge. If you would like more information about our EEO program or wish to file a complaint, please contact our EEO officer, Klaus Boeckers at [email protected]. For more details, view a copy of the EEOC's Know Your Rights poster. By submitting a job application to Geotab Inc. or its affiliates and subsidiaries (collectively, “Geotab”), you acknowledge Geotab’s collection, use and disclosure of your personal data in accordance with our Privacy Policy. The annual base salary for this position is the expected annual salary for this role, and may be subject to change. Geotab offers various perks and benefits and other compensation components that an individual may be eligible for. The actual base salary for this position depends on a variety of factors such as but not limited to skills, qualifications, education and overall experience, including the location the applicant lives while performing the job. This also includes equity with other team members and alignment with local market data. All offers of employment are contingent upon proof of eligibility to work and the individual's ability to pass a background check.
Hiring Range $83,200—$102,800 USD
Salary Context
This $83K-$102K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Geotab, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($93K) sits 56% below the category median. Disclosed range: $83K to $102K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Geotab AI Hiring
Geotab has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $102K - $102K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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