Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or Hugging Face. * Proficiency in designing and deploying machine learning models, particularly in ...
Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or Hugging Face. * Proficiency in designing and deploying machine learning models, particularly in ...
Strong programming skills in Python (or similar) and experience with AI/ML libraries (e.g., LangChain, OpenAI API, Hugging Face, PyTorch). * Understanding of agent-based modeling, multi-agent systems ...
Strong programming skills in Python (or similar) and experience with AI/ML libraries (e.g., LangChain, OpenAI API, Hugging Face, PyTorch). * Understanding of agent-based modeling, multi-agent systems ...
Hugging Face information
See Cheney, WA salary details
$9.34 - $10.49
3% of jobs
$10.49 - $11.64
5% of jobs
$11.64 - $12.79
6% of jobs
$13.81 is the 25th percentile. Wages below this are outliers.
$12.79 - $13.94
12% of jobs
$13.94 - $15.08
13% of jobs
The median wage is $15.84 / hr.
$15.08 - $16.23
17% of jobs
$16.23 - $17.38
9% of jobs
$18.44 is the 75th percentile. Wages above this are outliers.
$17.38 - $18.53
11% of jobs
$18.53 - $19.68
5% of jobs
$19.68 - $20.82
9% of jobs
$20.82 - $21.97
9% of jobs
$9
$16
$21
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What is the difference between Hugging Face vs Machine Learning Engineer?
| Aspect | Hugging Face | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Typically requires knowledge of NLP, deep learning, and Python; certifications are optional | Requires degrees in CS or related fields; experience with ML frameworks; certifications beneficial |
| Work Environment | Collaborative, research-focused, often in tech companies or startups | Development, deployment, and optimization of ML models in various industries |
| Employer & Industry Usage | Used by AI/ML companies, research labs, and open-source communities | Employed 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.
How much does Hugging Face pay?
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Posted 4 days ago
Job description
About Us
Our mission is to bring people together and connect them into a community to nurture each other. We aim to share a conducive environment, a joyous space to grow and excel; a world brimming with selfless love and enough kindness. We strive to enrich each of our lives with kaleidoscopic memories we make here - vibrant, lively, of all hues and colors.
Job Description
This is a remote position.
We are seeking a highly skilled and innovative Generative AI Automation Engineer to join our team. The ideal candidate will be responsible for designing, developing, and implementing automation solutions powered by Generative AI models. This role requires a combination of expertise in machine learning, natural language processing, software engineering, and automation frameworks to drive efficiency and innovation in business processes.
Key Responsibilities:
Generative AI Model Implementation:
Develop, fine-tune, and deploy Generative AI models (e.g., GPT, Stable Diffusion, DALL-E, etc.) for automation tasks.
Integrate pre-trained models or build custom models for specific use cases.
Automation Design and Development:
Design and implement AI-driven workflows and solutions to automate repetitive tasks and improve process efficiency.
Develop APIs, scripts, and tools for seamless integration of AI models into existing systems.
Data Management:
Collect, preprocess, and analyze large datasets for training and validating AI models.
Ensure data privacy and compliance with regulatory requirements during data handling.
System Integration:
Collaborate with software development and IT teams to integrate Generative AI solutions with enterprise systems.
Build and maintain pipelines for real-time AI inference and automation.
Monitoring and Optimization:
Continuously monitor AI automation solutions to ensure accuracy, efficiency, and reliability.
Optimize models and processes based on performance metrics and user feedback.
Research and Innovation:
Stay updated with the latest advancements in Generative AI and automation technologies.
Identify opportunities for implementing cutting-edge AI solutions to address business challenges.
Documentation and Collaboration:
Document technical designs, workflows, and implementation strategies.
Collaborate with cross-functional teams, including product managers, data scientists, and software engineers.
Required Qualifications:
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Strong programming skills in Python, with experience in frameworks like TensorFlow, PyTorch, or Hugging Face.
Proficiency in designing and deploying machine learning models, particularly in Generative AI.
Experience with automation tools (e.g., RPA, workflow orchestration tools).
Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and containerization technologies (Docker, Kubernetes).
Solid understanding of data structures, algorithms, and software design principles.
Strong analytical and problem-solving skills.
Excellent communication and teamwork abilities.
Preferred Qualifications:
Experience with NLP, image generation, or multimodal AI models.
Hands-on experience with APIs for AI services like OpenAI, Cohere, or Google AI.
Familiarity with prompt engineering and fine-tuning Generative AI models.
Knowledge of MLOps practices for deploying and maintaining AI solutions.
Previous experience in automation or workflow optimization projects.
Why Join Us?
Work with cutting-edge Generative AI technologies.
Collaborate with a team of forward-thinking innovators.
Make a tangible impact on the future of automation and AI-driven processes.
If you are passionate about leveraging Generative AI to create innovative automation solutions, we invite you to apply and be a part of our dynamic and growing team.
About EnthuZiastic
Sourced by ZipRecruiter
Industry
E-learning
Company size
11 - 50 Employees
Headquarters location
San Jose, CA, US
Year founded
2020