1 to 25 of 67 Remote Permanent Hugging Face Jobs

years of experience in Machine Learning, Data Science, or NLP/LLM. Strong Python skills; exposure to PyTorch/TensorFlow/Hugging Face. (Bonus) understand fundamentals of deep learning, LLMs, and MLOps, vector databases, embeddings, or retrieval-augmented generation (RAG). Loves solving complex problems and thrives … senior ML engineers, AI researchers, and founders. Freedom to experiment with state-of-the-art models, tools, and frameworks. Modern tech stack (Python, LangChain, Hugging Face, OpenAI API, Pinecone, Kubernetes, etc.). Flexible working — remote-first culture with in-person team sessions for collaboration. Career acceleration — opportunities ...

experience with microservices. A genuine interest in AI and LLMs — from using tools like Cursor and Copilot to experimenting with frameworks such as Hugging Face or LangChain is highly desirable. AI literacy and an interest in using AI tools to improve workflow and product outcomes. Degree in Computer … RabbitMQ). Knowledge of real-time streaming (Kafka Streams, Apache Flink, etc.). Exposure to big-data or machine-learning frameworks (TensorFlow, PyTorch, Hugging Face, LangChain). Experience with real-time streaming technologies (Kafka, Apache Storm). Understanding of infrastructure and DevOps (Terraform, Ansible, AWS, Kubernetes). Exposure ...

experience building and deploying Agentic AI or multi-agent systems Proficiency in Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong … years. Profile 29 recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput ...

Associate, GCP AI/ML Engineer). Vendor-specific CX/CCaaS certifications (Genesys Cloud, Amazon Connect). Specialist AI/ML certifications (TensorFlow, Hugging Face, OpenAI, or equivalent). Benefits This is your chance to join a friendly and passionate team that will motivate you to learn ...

domain expertise required — curiosity and adaptability are key Technical skillset: Must-have: Python, data science/ML libraries (pandas, NumPy, scikit-learn, XGBoost, PyTorch, Hugging Face), experiment design, evaluation metrics Nice-to-have: LLM fine-tuning, prompt engineering, SQL/PostgreSQL How We Work We live ...

3+ years in software engineering, AI engineering, or machine learning roles. Strong programming skills (Python preferred). Experience with LLM integration (e.g., OpenAI, Anthropic, Hugging Face). Understanding of agentic frameworks, prompt engineering, and orchestration tools. Familiarity with APIs, databases, and cloud infrastructure (AWS, Azure, or GCP). ...

3+ years in software engineering, AI engineering, or machine learning roles. Strong programming skills (Python preferred). Experience with LLM integration (e.g., OpenAI, Anthropic, Hugging Face). Understanding of agentic frameworks, prompt engineering, and orchestration tools. Familiarity with APIs, databases, and cloud infrastructure (AWS, Azure, or GCP). ...

5+ years of industry experience in machine learning, deep learning, or applied AI Proficiency in Python, PyTorch/TensorFlow, and modern ML toolchains (e.g., Hugging Face, OpenAI API, or custom training frameworks). Experience building data pipelines for model training, evaluation, and continuous improvement. Understanding of retrieval-augmented ...

using advanced Python techniques (monkey patching, decorators, introspection). ML Framework Integration: Build and maintain deep integrations with leading AI libraries (PyTorch, TensorFlow, Keras, Hugging Face, vLLM, scikit-learn). Packaging & Distribution: Manage the build lifecycle, creating and optimizing Python wheels and Eggs, and managing releases to PyPI ...

using advanced Python techniques (monkey patching, decorators, introspection). ML Framework Integration: Build and maintain deep integrations with leading AI libraries (PyTorch, TensorFlow, Keras, Hugging Face, vLLM, scikit-learn). Packaging & Distribution: Manage the build lifecycle, creating and optimizing Python wheels and Eggs, and managing releases to PyPI ...

using advanced Python techniques (monkey patching, decorators, introspection). ML Framework Integration: Build and maintain deep integrations with leading AI libraries (PyTorch, TensorFlow, Keras, Hugging Face, vLLM, scikit-learn). Packaging & Distribution: Manage the build lifecycle, creating and optimizing Python wheels and Eggs, and managing releases to PyPI ...

using advanced Python techniques (monkey patching, decorators, introspection). ML Framework Integration: Build and maintain deep integrations with leading AI libraries (PyTorch, TensorFlow, Keras, Hugging Face, vLLM, scikit-learn). Packaging & Distribution: Manage the build lifecycle, creating and optimizing Python wheels and Eggs, and managing releases to PyPI ...

using advanced Python techniques (monkey patching, decorators, introspection). ML Framework Integration: Build and maintain deep integrations with leading AI libraries (PyTorch, TensorFlow, Keras, Hugging Face, vLLM, scikit-learn). Packaging & Distribution: Manage the build lifecycle, creating and optimizing Python wheels and Eggs, and managing releases to PyPI ...