2

Remote Generative Ai Jobs in Arizona (NOW HIRING)

$50/hr

Proven knowledge and expertise in generative AI applications, including deep generative modeling ... Working Location Location flexible (Tokyo, NYC, remote) The target hourly rate for this internship ...

This is a remote work opportunity Insight at a Glance * 14,000+ engaged teammates globally * $8.2 ... Demonstrated experience with Generative AI and Large Language Models (LLMs), with direct expertise ...

$15 - $20/hr

Proficiency with generative AI * Video editing skills * Experience with motion graphics or ... Remote Duration: 10-12 weeks (Summer 2025)

Guide the adoption of emerging technologies, including Generative AI Development Toolchains, LLMs ... We offer competitive compensation, a remote-first lifestyle, and career growth opportunities across ...

... IL, Remote-MI, Remote-NJ, St. Louis, Missouri Details Kemper is one of the nation's leading ... Lead enterprise implementation of Generative AI, Machine Learning, Intelligent Automation, and ...

US Remote Dallas and Scottsdale preferred Overview: The Senior Director, Finance IT is a senior ... Build the Finance IT roadmap for Generative AI and Copilot integration across finance workflows.

Time Type: Full time Remote Type: Job Family Group: Human Resources Summary: The Manager, Talent ... Generative AI tools (e.g., Microsoft Copilot, ChatGPT, AI-assisted analytics tools) * Advanced ...

Senior Software Engineer

Tempe, AZ ยท Remote

$91K - $163K/yr

If you live near Tempe, AZ, you will enjoy the flexibility of a hybrid-remote role as you take on ... Design, develop, and productionize machine learning and generative AI solutions supporting use ...

Senior Software Engineer

Phoenix, AZ ยท On-site +1

$121K - $160K/yr

This is a remote position anywhere in the USA. Meet the Team Our software engineering team develops ... Ability to use AI/ML or generative AI to solve security problems, such as automated threat ...

New

Senior IT Auditor

Phoenix, AZ ยท On-site +1

$93K - $122K/yr

Proven commitment to continuous learning, ability to work as part of a team using remote ... Data analytics and visualization, automation, and generative artificial intelligence (Gen AI ...

Remote Generative Ai information

What are common challenges faced by remote generative AI professionals and how can they be addressed?

Remote Generative AI professionals often face challenges such as collaborating effectively across time zones, ensuring data security, and staying updated with rapidly evolving AI technologies. To overcome these, it's important to establish clear communication channels, utilize version control and collaboration tools, and participate in regular team meetings. Additionally, investing time in continuous learning through online courses and AI research communities can help professionals stay current with industry advancements.

What skills and qualifications are needed to thrive as a remote generative AI specialist?

To thrive as a Remote Generative AI Specialist, you need strong expertise in machine learning, deep learning, and programming languages like Python, often supported by a degree in computer science or a related field. Proficiency with frameworks such as TensorFlow or PyTorch, cloud platforms, and relevant certifications (e.g., Google Cloud ML Engineer) is highly beneficial. Effective problem-solving, self-motivation, and clear communication are crucial for collaborating remotely and driving innovative AI solutions. These skills ensure you can develop, deploy, and improve generative AI models efficiently in distributed work environments.

What is the difference between Remote Generative Ai vs Data Scientist?

AspectRemote Generative AiData Scientist
Required CredentialsKnowledge of AI/ML, programming skills, familiarity with NLP and deep learningStatistics, programming, data analysis, often a degree in CS, stats, or related fields
Work EnvironmentRemote, collaborative teams, AI research labs, tech companiesRemote or on-site, data analysis teams, research or business units
Industry UsageDeveloping AI models, creating generative content, NLP applicationsAnalyzing data, building predictive models, informing business decisions

Remote Generative Ai specialists focus on creating AI models that generate content, requiring expertise in AI/ML and programming. Data Scientists analyze data to extract insights and build models, often with similar technical backgrounds. While both roles may work remotely and in tech industries, their core functions differ: one develops generative AI systems, the other interprets data for strategic insights.

What is a remote generative AI?

A Remote Generative AI job involves working with artificial intelligence systems that can create new content, such as text, images, or music, from data. These roles are performed remotely, allowing professionals to work from anywhere while developing, training, and deploying generative models like GPT or DALL-E. Job responsibilities may include data preparation, model training, evaluation, and integrating generative AI solutions into products or services. Professionals in this field often collaborate with teams online using cloud-based tools and communication platforms.

What are the most commonly searched types of Generative Ai jobs in Arizona?

The most popular types of Generative Ai jobs in Arizona are:

What job categories do people searching Remote Generative Ai jobs in Arizona look for?

The top searched job categories for Remote Generative Ai jobs in Arizona are:

What cities in Arizona are hiring for Remote Generative Ai jobs?

Cities in Arizona with the most Remote Generative Ai job openings:

Infographic showing various Remote Generative Ai job openings in Arizona as of August 2026, with employment types broken down into 79% Full Time, 18% Part Time, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Senior AI Engineer / Data Scientist

Koantek

Chandler, AZ โ€ข Remote

Contractor

Re-posted 25 days ago


Job description

Senior AI Engineer / Data Scientist (Consulting) Location: United States (Remote) Employment Type: Full-Time / Contract Experience Level: Senior About the Role: We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation. You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base.

Key Responsibilities: * Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions. * MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability. * GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings.

* Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark. * Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences. Required Qualifications: * 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory).

* 3+ years of experience in a customer-facing consulting or Solutions Architect role. * Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment). * Solid hands-on experience with containerization (Docker) and data pipeline orchestration.

* Proven track record of deploying Generative AI and NLP solutions for client applications. * Excellent verbal and written communication skills. Preferred Qualifications: * Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks.

* Deep knowledge of large-scale data processing and distributed machine learning techniques. * A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.