26 to 50 of 175 Remote Hugging Face Jobs

systems. Additional skills include leadership in agile environments, strong communication, mentorship abilities, expertise in RAG architectures, Transformer-based LLMs, model optimization, programming in Python with frameworks like PyTorch, TensorFlow, Hugging Face, LangChain, experience with containerization tools, cloud deployment (preferably Azure), and familiarity with databases and data platforms. OUR VALUES: CURIOSITY, OPTIMISM, AMBITION, EMPATHY We foster a team culture More ❯

related: we use Python, SQL, Unix-based systems, git, and github for collaboration and review). Machine/Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases). An understanding of Language Models, using and training/fine-tuning and a familiarity with industry-standard LM families. More ❯

experience (we use Python, SQL, Unix-based systems, git, and github for collaboration and review). Machine/Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases). An understanding of Language Models, using and training/fine-tuning and a familiarity with industry-standard LM families. More ❯

experience (we use Python, SQL, Unix-based systems, git, and github for collaboration and review). Machine/Deep Learning development skills, including experiment tracking (we use AWS SageMaker, Hugging Face, transformers, PyTorch, scikit-learn, Jupyter, Weights & Biases). An understanding of Language Models, using and training/fine-tuning and a familiarity with industry-standard LM families. More ❯

mentoring teams. Expert-level programming skills in Python or another high-level language commonly used in machine learning. Expertise in NLP and Machine Learning frameworks and libraries (e.g., PyTorch, HuggingFace, LangChain, spaCy, NLTK, scikit-learn). Experience with information retrieval techniques and structured data extraction from unstructured sources. Hands-on experience with LLM APIs for pre-processing, fine-tuning, and More ❯

Amazon Bedrock for deploying foundation models and managing scalable GenAI workloads. Proficiency in fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker JumpStart and Hugging Face on AWS. Experience leveraging Amazon Q (formerly AWS CodeWhisperer) Business and Developer for AI-powered coding productivity and automation. Machine Learning Frameworks and Pipelines: In-depth knowledge … to-date on Generative AI advancements such as RLHF (Reinforcement Learning with Human Feedback), foundation model fine-tuning, and hybrid AI architectures. Familiarity with integrating open-source tools like Hugging Face Transformers and AWS-native solutions. Knowledge of Multi-Cloud AI Tools: Good knowledge and understanding of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and … AWS Solutions Architect Professional or hold the AWS Machine Learning Specialty certification? * Select... Have you architected production-grade Generative AI solutions using services like Amazon Bedrock or SageMaker with Hugging Face? * Select... Have you built or optimized distributed ML pipelines using AWS services like SageMaker Pipelines, Step Functions, Lambda, and EKS or Fargate? * Select... Do you have experience More ❯

and Demonstrate Build proof-of-concepts (PoCs) or demos to validate solution approaches and inspire client confidence. Showcase AI capabilities using relevant tools (e.g. Power BI + Azure ML, Hugging Face, LangChain, GPT APIs). 5. Track Industry Trends and Use Cases Stay ahead of market trends in Generative AI, Responsible AI, and emerging AI use cases across … ML concepts: supervised and unsupervised learning, GenAI, LLMs, embeddings, MLOps, vector search. Experience designing solutions using tools such as Azure ML, AWS SageMaker, Google Vertex AI, Databricks, LangChain, and Hugging Face. Ability to develop architecture artefacts (e.g., HLDs), estimate effort and cost, and contribute to proposal writing. Strong storytelling and presentation skills; able to build confidence with senior business More ❯

including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to More ❯

Skills Required: LLM Experience : Production experience with GPT, Claude, LLaMA, including fine-tuning, prompt engineering, and deployment Python Proficiency : Strong skills with ML frameworks (TensorFlow, PyTorch) and LLM tools (Hugging Face, LangChain, OpenAI APIs) Vector Understanding : Solid knowledge of embeddings, vector databases (Pinecone, Weaviate, FAISS), and RAG pipelines NLP Fundamentals : Text preprocessing, language modelling, and semantic similarity Cloud More ❯

Proven track record of developing and deploying LLM-based solutions in an enterprise setting as a senior/staff scientist. Proficiency in Python and libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and spaCy. Strong understanding of LLM architectures (e.g., GPT, BERT, T5) and experience fine-tuning them for specific tasks. Demonstrable experience in utilising different model architectures More ❯

or related technical field 2+ years of experience in AI/ML development with a focus on practical applications Strong proficiency in Python and relevant AI libraries (TensorFlow, PyTorch, Hugging Face) Hands-on experience with workflow automation platforms like N8N, AirTable, and proven track-record. Experience with AI agent development and testing methodologies using Google ADK, LangGraph, Llamaindex More ❯

real impact. Implementing MLOps best practices for seamless model deployment, monitoring, and iteration. What We’re Looking For: Strong Python skills – You know your way around TensorFlow, PyTorch, or Hugging Face. Experience with LLMs, NLP, or recommendation algorithms . Familiarity with MLOps, APIs, and scalable cloud solutions . Passion for AI-powered personalisation and making tech feel more human. More ❯

cost and delivery timelines What we're looking for 4-5 years relevant experience Proficiency in Python or similar programming languages Experience with GenAI tools (LangGraph, CrewAI, OpenAI APIs, Hugging Face) ML fundamentals and LLM expertise (prompt engineering, cost optimization) Cloud platforms (AWS/GCP/Azure) and infrastructure-as-code DevOps tooling (CI/CD, Docker, Kubernetes More ❯

cost and delivery timelines What we're looking for 4-5 years relevant experience Proficiency in Python or similar programming languages Experience with GenAI tools (LangGraph, CrewAI, OpenAI APIs, Hugging Face) ML fundamentals and LLM expertise (prompt engineering, cost optimization) Cloud platforms (AWS/GCP/Azure) and infrastructure-as-code DevOps tooling (CI/CD, Docker, Kubernetes More ❯

cost and delivery timelines What we're looking for 4-5 years relevant experience Proficiency in Python or similar programming languages Experience with GenAI tools (LangGraph, CrewAI, OpenAI APIs, Hugging Face) ML fundamentals and LLM expertise (prompt engineering, cost optimization) Cloud platforms (AWS/GCP/Azure) and infrastructure-as-code DevOps tooling (CI/CD, Docker, Kubernetes More ❯

including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow). Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to More ❯

Looking For: 2+ years of experience in software engineering (or strong portfolio/projects), ideally in Python. Exposure to AI/ML tools, APIs, or foundational models (e.g. OpenAI, Hugging Face, LangChain). Familiarity with cloud services (e.g. AWS, GCP, or Azure) and containerization (e.g. Docker). Interest in or experience with building microservices , APIs, or distributed systems. More ❯

including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models. Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications. Day to Day Conduct research More ❯

AI Infrastructure & MLOps: Experience with cloud AI services, model deployment, monitoring, and CI/CD pipelines for ML models (MLOps best practices). Example Tools & Technologies: Frameworks & Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow More ❯

Practical knowledge of AI/ML technologies and their applications, including understanding language models (LLMs), and experience with training and fine-tuning LLMs using frameworks like TensorFlow, PyTorch, or Hugging Face Transformers. Good understanding of programming/scripting: (e.g., Python, Go) for customizing solutions, creating scripts, or automating tasks. Experience with AI relevant infrastructure, including Networking (InfiniBand and More ❯

d love to hear from you! Requirements Proficiency in at least one programming language. I.e Python, C#, JavaScript, Kotlin, Java, Go.. Experience with Gen AI tools (e.g LangGraph, CrewAI, Hugging Face, OpenAI APIs etc) Experience in building and optimising systems for performance, scalability, and reliability Understanding of ML fundamentals (training, evaluation, deployment) and LLM-specific challenges (prompt engineering More ❯

projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the ability to translate technical insights More ❯

projects—from data exploration and model development to deployment and evaluation. Expert-level proficiency in Python and ML libraries such as Scikit-learn, XGBoost, TensorFlow, PyTorch, and/or Hugging Face. Experience working with large-scale datasets, cloud platforms (AWS/GCP), and ML Ops tools is a plus. Excellent communication skills and the ability to translate technical insights More ❯

a leadership role . Strong understanding of software architecture, design patterns, and cloud-native development (preferably AWS or GCP). Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face) and deploying models in production. Proficiency in building RESTful APIs, microservices, and working with asynchronous frameworks (e.g., FastAPI, asyncio). Familiarity with containerization (Docker, Kubernetes) and CI More ❯

training frameworks, libraries and tools. Deep knowledge of state-of-the-art transformer and non-transformer modifications aimed at enhancing intelligence, efficiency and scalability. Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment. More ❯