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Hugging Face Jobs in Arizona (NOW HIRING)

Arkhya Tech

GenAI Solutions Leader

Phoenix, AZ · On-site

Hands-on with GenAI and agentic AI (LLMs, diffusion models, RAG, tool use/agents); familiarity with OpenAI Azure, Hugging Face, LangChain/LangGraph, ADK, vector databases. Experience with MLOps ...

Forhyre

Prompt Engineers

Phoenix, AZ

Familiarity with machine learning frameworks and libraries like TensorFlow, PyTorch, or Hugging Face. * Strong analytical and problem-solving skills with a keen eye for detail. * Excellent ...

Tata Consultancy Service Limited

AI Engineer

Phoenix, AZ · On-site

$100K - $120K/yr

... Hugging Face. • Practical knowledge of model orchestration frameworks (e.g., LangChain, LlamaIndex, CrewAI), Familiarity with vector databases • Experience with cloud platforms (AWS, Azure AI ...

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99K - $131K/yr

Model-level work using PyTorch and the Hugging Face ecosystem (embeddings, fine-tuning, inference tooling), with some exposure to TensorFlow * Strong schema, validation, and state management ...

American Express

Senior AI Engineer I

Phoenix, AZ · On-site

$123K - $215K/yr

Experience with machine learning frameworks such as PyTorch, TensorFlow, and Hugging Face. * Bachelor's degree in Computer Science, Computer Engineering, Data Science, and/or comparable experience ...

TSMC

Senior AI Model Fine-Tuning Engineer

Phoenix, AZ

$128K - $176K/yr

Experience with popular transformer architectures and frameworks like Hugging Face, TensorFlow, or PyTorch. * Deep understanding of LLM behaviors, including instruction-following, task completion ...

TSMC

Senior AI Model Fine-Tuning Engineer

Phoenix, AZ · On-site

$128K - $176K/yr

Experience with popular transformer architectures and frameworks like Hugging Face, TensorFlow, or PyTorch. * Deep understanding of LLM behaviors, including instruction-following, task completion ...

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How much do hugging face jobs pay per hour?

As of Jul 22, 2026, the average hourly pay for hugging face in Arizona is $14.40, according to ZipRecruiter salary data. Most workers in this role earn between $12.12 and $17.02 per hour, depending on experience, location, and employer.

Can you make money on Hugging Face?

Hugging Face is a platform that offers opportunities for data scientists, machine learning engineers, and developers to monetize their skills through jobs, freelance projects, or contributing to open-source models. Earning potential depends on the type of work, experience, and whether you are employed directly or working independently. Building a strong portfolio and expertise in NLP and AI tools can increase income opportunities on the platform.

Which 3 jobs will survive AI?

Jobs that require complex human interaction, creativity, and critical thinking, such as healthcare professionals, educators, and skilled tradespeople, are likely to persist despite AI advancements. These roles often involve emotional intelligence, nuanced judgment, and hands-on skills that are difficult for AI to replicate. Continuous learning and adaptability remain important for job security in an evolving technological landscape.

What are Hugging Face jobs?

Hugging Face jobs refer to employment opportunities at the company focused on developing and maintaining open-source machine learning tools, especially in natural language processing. Roles may include software engineering, research, data science, and product management, often requiring skills in Python, deep learning frameworks, and collaboration in a tech environment.

How much do Hugging Face engineers make?

Hugging Face engineers' salaries vary based on experience, role, and location, but generally range from $100,000 to $180,000 annually. Senior positions and specialized roles in machine learning or software engineering tend to offer higher compensation, often including stock options and benefits.

What is the difference between Hugging Face vs Machine Learning Engineer?

AspectHugging FaceMachine Learning Engineer
Required CredentialsTypically requires knowledge of NLP, deep learning, and Python; certifications are optionalRequires degrees in CS or related fields; experience with ML frameworks; certifications beneficial
Work EnvironmentCollaborative, research-focused, often in tech companies or startupsDevelopment, deployment, and optimization of ML models in various industries
Employer & Industry UsageUsed by AI/ML companies, research labs, and open-source communitiesEmployed across tech, finance, healthcare, and other sectors implementing ML solutions

Hugging Face primarily focuses on NLP tools, libraries, and open-source models, serving as a platform for AI research and development. Machine Learning Engineers develop, implement, and optimize ML models across various domains. While Hugging Face offers resources and tools that ML Engineers use, the roles differ: Hugging Face is a platform, whereas Machine Learning Engineer is a job role involving hands-on model development and deployment.

What are popular job titles related to Hugging Face jobs in Arizona? For Hugging Face jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Hugging Face jobs in Arizona look for? The top searched job categories for Hugging Face jobs in Arizona are:
What cities in Arizona are hiring for Hugging Face jobs? Cities in Arizona with the most Hugging Face job openings:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Arizona as of July 2026, with employment types broken down into 80% Full Time, 19% Part Time, and 1% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution, with an average salary of $29,961 per year, or $14.4 per hour.
GenAI Solutions Leader

GenAI Solutions Leader

Arkhya Tech

Phoenix, AZ • On-site

Other

Posted 27 days ago

Job description

AI Evangelist/AI Expert/ GenAI Solutions Leader

Location : Phoenix AZ - Onsite

Full-Time

Lead platform releases, feature rollouts, and adoption initiatives in partnership with product and engineering teams.

o Architect and execute go to market strategies spanning onboarding, training, documentation, and ongoing support.

Customer Enablement & Training o Conduct workshops, office hours, and hands on pair programming while maintaining self service resources (SDKs, guides, playbooks) to drive adoption and reduce time to value. o Create scalable enablement assets and tailor training approaches based on a deep understanding of customer workflows and pain points.

Solution Strategy & Feedback Loop o Establish tight feedback loops with end users to surface insights that shape roadmap direction, influence implementation, and drive usability improvements. o Translate business problems into actionable solution architectures partnering with platform teams on patterns, reusable accelerators, acceptance criteria, and reference architectures to standardize solution delivery.

o Stay current with industry trends in MLOps/LLMOps, GenAI, agentic frameworks, and cloud optimization.

Stakeholder Relationship & Communication o Build

Required Qualifications:

4+ years of Artificial Intelligence experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

3+ years across product/solution management, program delivery, or technical product ownership for AI/ML platforms, or cloud-native solutions.

3+ years hands-on with cloud technologies (Google Cloud Platform, or Azure) and container orchestration (Docker, Kubernetes/OpenShift). Desired Qualifications

5+ years across the AI/ML lifecycle: data management, feature engineering, model development, deployment, monitoring/observability, and model risk/governance.

Experience in large enterprise environments (regulated industries preferred) and building platforms at scale.

Hands-on with GenAI and agentic AI (LLMs, diffusion models, RAG, tool use/agents); familiarity with OpenAI Azure, Hugging Face, LangChain/LangGraph, ADK, vector databases.

Experience with MLOps/LLMOps tooling and practices (model registry, CI/CD, feature store, prompt/chain/versioning, evaluation, guardrails, monitoring).

Strong communication skills with the ability to influence senior stakeholders and simplify complex technical

Regards,

Bhupendra

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