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Hugging Face Jobs in Orange, VA (NOW HIRING)

Appvion, LLC

Artificial Intelligence Architect

Keswick, VA

$63 - $81/hr

PyTorch, TensorFlow, scikit-learn, and Hugging Face * MLOps: Docker, Kubernetes, CI/CD, MLflow, and model registries * Cloud & data: AWS, Azure, GCP, Spark, Airflow, and feature stores * LLM, GenAI ...

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How much do hugging face jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for hugging face in Orange, VA is $14.87, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $17.60 per hour, depending on experience, location, and employer.

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 cities near Orange, VA are hiring for Hugging Face jobs?

Cities near Orange, VA with the most Hugging Face job openings:

Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Orange, VA as of June 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $30,930 per year, or $14.9 per hour.

Artificial Intelligence Architect

Keswick, VA

Appvion, LLC
Paper Manufacturing • 1 - 5K employees

8.3

Company rating: 8.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Good employer

Good training

Learn more about Appvion, LLCexternal

$63 - $81/hr

Full-time

Re-posted 7 days ago

Job description

About the Role

We're hiring an AI Architect to define the technical foundation for all our AI/ML systems including architecture standards, platform decisions, and quality gates that let us deliver scalable, secure, and governed AI solutions tied directly to business outcomes. You'll sit at the intersection of engineering, data, and business strategy, designing the systems and setting the standards that accelerate AI adoption across the enterprise.

What You'll Do

  • Design the enterprise AI/ML architecture, including reference patterns and multi-entity / multi-tenant architectures with governed data boundaries
  • Evaluate and select AI platforms, frameworks, and cloud services
  • Establish technical standards for model development, testing, and deployment
  • Design agentic search and retrieval systems for enterprise knowledge grounding
  • Review and approve architecture for all AI use cases after they reach production
  • Define data architecture requirements for ML pipelines
  • Lead build vs. buy evaluations for AI tooling
  • Mentor technical team members and drive engineering excellence
  • Stay current on AI/ML technology trends and assess their relevance to our roadmap

Qualifications

  • 8+ years in software or data architecture, with 4+ years focused on ML systems
  • Deep expertise in cloud platforms (AWS, Azure, or GCP) and their ML services
  • Proven experience designing production ML pipelines at enterprise scale
  • Strong understanding of MLOps, model monitoring, and deployment patterns
  • Experience with both traditional ML and modern LLM/GenAI architectures
  • Familiarity with core enterprise infrastructure architecture

Skills

  • Languages: Python, SQL, and Scala for ML and data engineering
  • ML frameworks: PyTorch, TensorFlow, scikit-learn, and Hugging Face
  • MLOps: Docker, Kubernetes, CI/CD, MLflow, and model registries
  • Cloud & data: AWS, Azure, GCP, Spark, Airflow, and feature stores
  • LLM, GenAI & agentic search: RAG, fine-tuning, prompt engineering, vector databases, query planning, tool use, retrieval orchestration, and multi-step reasoning
  • Responsible AI: governance, model monitoring, and security by design
  • Solution mindset: design thinking, trade-off analysis, and pragmatic delivery

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