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Hugging Face Jobs in Boca Raton, FL (NOW HIRING)

Infosys

Gen AI / Agentic AI Lead

Palm Beach, FL · On-site

$135K - $166K/yr

... Hugging Face Transformers, LangChain, PyTorch). • Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search). • Familiarity with cloud platforms and Gen AI services ...

Medical Air Services Association

AI Fellow

Fort Lauderdale, FL · On-site

$46K - $63K/yr

A GitHub, a side project, a Hugging Face space, a hackathon submission, a Discord bot, a Chrome extension you built for fun-anything that shows you actually build, not just study. * Real fluency with ...

Deloitte

Oracle Generative AI Manager

Boca Raton, FL · On-site

Knowledge of modern AI frameworks (LangChain, LangGraph, NVIDIA NIM, Hugging Face). * Proven track record of leading AI/ERP transformation programs in large enterprises. * Strong communication skills ...

Apex Systems

AI engineer

Fort Lauderdale, FL · Remote

$109K - $131K/yr

Strong proficiency in Python and at least one ML framework such as PyTorch, TensorFlow, scikit-learn, or Hugging Face. Experience with data pipeline tools and frameworks like Spark, Airflow, dbt, or ...

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How much do hugging face jobs pay per hour?

As of Jun 7, 2026, the average hourly pay for hugging face in Boca Raton, FL is $14.67, according to ZipRecruiter salary data. Most workers in this role earn between $12.31 and $17.36 per hour, depending on experience, location, and employer.

What professions make 500,000 a year?

Professions that can earn $500,000 or more annually include senior roles such as CEOs, investment bankers, specialized surgeons, and successful entrepreneurs. High earnings often require extensive experience, advanced skills, and often involve leadership or highly specialized knowledge in their fields.

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 Boca Raton, FL? For Hugging Face jobs in Boca Raton, FL, the most frequently searched job titles are:
What job categories do people searching Hugging Face jobs in Boca Raton, FL look for? The top searched job categories for Hugging Face jobs in Boca Raton, FL are:
What cities near Boca Raton, FL are hiring for Hugging Face jobs? Cities near Boca Raton, FL with the most Hugging Face job openings:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Boca Raton, FL as of May 2026, with employment types broken down into 96% Part Time, and 4% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $30,510 per year, or $14.7 per hour.

Principal Engineer Data & AI

US DEFAULT GROUP INC

Boca Raton, FL • On-site

Full-time

Posted 8 days ago

Job description

Principal Engineer – Data & AI

The Principal Engineer – Data & AI is a senior, hands‑on lead engineer responsible for defining and implementing enterprise‑scale AI, data, and automation architectures and solutions that drive digital transformation across the organization. This role reports to the Director of AI and Automation and supports the technical direction, architecture standards, and delivery of advanced automation, data platforms, and AI‑driven solutions. This is an engineering‑first role, focused on building and operating production‑grade systems—not a research‑only data science position. The role operates in a highly collaborative environment with AI engineers, automation engineers or data scientists, legal domain experts, and business leaders to deliver AI automation, Gen AI, and advanced analytics solutions at scale.

Key Responsibilities

Architecture & Platform Leadership

  • Drives and supports architecture decisions for AI, automation, and data platforms.
  • Define and maintain reference architectures, design standards, and reusable frameworks.
  • Design automation involving external applications or sites, APIs, and internal applications through scalable microservices.
  • Lead implementation of robust lakehouse/warehouse supporting analytics, automation, and AI workloads for agentic AI.
  • Establish patterns for batch and streaming pipelines, event‑driven architectures, and scalable data access.

Data Engineering & AI Enablement

  • Design and build robust data pipelines using technologies such as Azure Data Factory, Azure Data Lake, Snowflake, Databricks, SQL, Spark, Python, or other similar technologies.
  • Implement strong data quality, lineage, observability, governance, and auditability standards.
  • Deliver curated datasets, semantic models, and data products for analytics and downstream systems.
  • Lead development of Intelligent Document Processing (IDP), RAG pipelines, GenAI‑driven architectures, and NLP based querying.
  • Make the enterprise context and data available easy for business consumption and decisions.
  • Develop and identify meaningful insights through “big data”, assists in the creation of required ETL pipelines and data structures for Azure Data Lake, Databricks or snowflake, and Data Factory.

AI, Automation & GenAI

  • Design and deploy AI agents, GenAI models, IDP models, and workflow‑driven AI automation.
  • Implement and manage MLOps and LLMOps pipelines for training, deployment, monitoring, and governance.
  • Integrate AI/ML solutions into systems using APIs, microservices, queues, MCP, and containers.
  • Build secure, compliant RAG architectures with vector search and prompt/version management.

Engineering Execution & Governance

  • Lead the full lifecycle: discovery, architecture, development, testing, deployment, and support.
  • Ensure adherence to enterprise security, DevOps, compliance, and data governance standards.
  • Monitor and optimize performance, reliability, and cost of AI/automation platforms.
  • Collaborate with the department leader and technical lead to drive technical direction and architecture decisions aligning with standard tech stacks.

Required Qualifications

  • Bachelor’s degree in Computer Science, Data Science, Analytics, or related field (Master’s degree is preferred).
  • 7+ years of software, data, AI or platform engineering experience.
  • 5+ years building data engineering, AI automation, or cloud‑native solutions.
  • Proven experience delivering production AI systems, including ML, MCP servers, GenAI, LLM, IDP, NLP, and RAG‑based architectures.
  • Strong hands‑on expertise in Python, SQL, React, C#, Java, and other frameworks.
  • Strong experience defining enterprise data structures, data migration across tools, metadata catalogs, and data governance standards, with hands‑on implementation of multi‑tier (raw, curated, consumption) data lake or warehouse architectures.
  • Deep experience with most of the Microsoft Azure Services (Data platforms, Docker, Functions, Kubernetes, Azure Containers, Function Apps, ASB, App Services, GitHub, and ML studio).
  • Strong stakeholder communication and technical leadership skills.


Preferred Skills

  • Azure Data Factory, ADLS Gen2, Synapse/Fabric, Azure Databricks, Snowflake
  • SQL, Python, Java, C#, React, Power Automate, Workato
  • Playwright, Azure ML, Azure OpenAI, Document Intelligence
  • Docker, Kubernetes, GitHub Actions, CI/CD, semantic models, vector databases
  • LangChain, Hugging Face, scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face, OpenCV, NLP, MCP, NLTK, Airflow, Spark, Mistral, ML studio, shell scripting, UAMI, Yaml, and advanced libraries and other open-sources.
  • Event‑driven systems (Service Bus, Container Apps, AKS, ACS, KEDA, Event Grid, etc.)