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Pytorch Huggingface Jobs in Missouri (NOW HIRING)

Jobtailor

Senior Director, AI Engineering

California, MO · On-site

$190 - $240/hr

Make high judgment build‑vs‑buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more.

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Pytorch Huggingface information

What are the key skills and qualifications needed to thrive as a PyTorch Huggingface engineer?

To thrive as a PyTorch Hugging Face Engineer, you need a strong background in deep learning, Python programming, and experience with machine learning frameworks, supported by a relevant degree such as computer science or engineering. Familiarity with PyTorch, Hugging Face Transformers library, version control systems like Git, and often cloud platforms (e.g., AWS, GCP) is essential, with certifications in machine learning or cloud technologies being advantageous. Strong problem-solving skills, collaboration, and clear communication help you effectively design, implement, and optimize NLP models in cross-functional teams. These skills ensure you can build state-of-the-art AI solutions efficiently, troubleshoot complex challenges, and deliver impactful results in the fast-evolving field of natural language processing.

What is the difference between Pytorch Huggingface vs Machine Learning Engineer?

AspectPytorch HuggingfaceMachine Learning Engineer
CredentialsProficiency in Python, deep learning frameworks, familiarity with NLP librariesDegree in CS, data science, or related field; experience with ML models
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLP and deep learningTech companies, consulting firms, R&D departments across industries
UsageDeveloping NLP models, fine-tuning transformers, deploying AI solutionsDesigning, building, and deploying ML models across various domains

While Pytorch Huggingface specializes in NLP model development using transformer architectures, Machine Learning Engineers work across diverse ML applications. Pytorch Huggingface skills are often part of a Machine Learning Engineer's toolkit, but the roles differ in scope and focus.

What is a PyTorch Huggingface engineer?

PyTorch Hugging Face developers are professionals who specialize in building and deploying machine learning and natural language processing (NLP) models using PyTorch, an open-source deep learning framework, and the Hugging Face library, which provides a wide range of pre-trained models and tools for NLP tasks. These developers create, fine-tune, and implement models for tasks like text classification, question answering, and language generation. Their expertise includes working with model architectures such as BERT, GPT, and others, as well as integrating models into applications or research projects.

How do PyTorch Huggingface engineers typically collaborate with data scientists and researchers in a project setting?

PyTorch Huggingface engineers often work closely with data scientists and researchers to implement, fine-tune, and deploy state-of-the-art machine learning models. Collaboration involves regular discussions to understand project objectives, translating research ideas into efficient code, and iterating on model performance. Engineers are responsible for optimizing model pipelines, integrating new features, and ensuring compatibility with the Huggingface ecosystem. Effective communication and teamwork are essential, as projects usually require frequent feedback loops and joint problem-solving sessions.

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Pytorch Huggingface jobs near you

Machine Learning / Prompt Engineer (Ollama, Langchain, hugging face and tensor flow)

RIT Solutions, Inc.

Chesterfield, MO • On-site

Full-time

Re-posted 8 days ago

Job description

Job Summary:
RIT Solutions, Inc. is seeking a Machine Learning / Prompt Engineer to work on innovative AI solutions. The role involves crafting effective prompts, model training, and optimization using various machine learning libraries and frameworks.
Qualifications:
Required:
• Ollama
• Langchain
• Hugging face
• Tensor Flow
• Expert - crafting effective prompts for tasks like role-playing, summarization, classification, reasoning, and creative generation
• Machine learning (model training, fine-tuning) with python, ollama, langchain, huggingface
• Libraries (e.g. TensorFlow, PyTorch, pandas, ffmpeg, scikit-learn)
• Expert: design, optimize, model training, fine-tuning, LoRA, evaluation, and optimization
• Generative AI/LLM (Large Language Model, type of machine learning model specifically designed to process and generate human-like text.)
• Deep understanding of LLM behavior, hyperparameters, tokenization, and context windows
• Retrieval augmented generation
• testing experience.
Company:
Jobdiva Job Portal: https://www1.jobdiva.com/candidates/myjobs/searchjobsdone.jsp?a=xbjdnwgjodtga1y1im2g881fkkeiwd0775lbvq8yqgps8vb2q36w2vj1ga6xxork&compid=-1 Recruitment (contingency search and campus selection). Founded in 2019, the company is headquartered in Arlington, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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