1 to 25 of 376 Hugging Face Jobs in the UK

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 … 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 … 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 More ❯

microservices. The ideal candidate will be adept at developing end-to-end applications to serve AI/ML models, including those from platforms like Hugging Face, and will work with a modern AWS-based toolchain. Your core responsibilities include: Serve as the day-to-day liaison between Data … helping them understand AI-specific requirements and performance nuances. Design, develop, and deploy standalone and micro-applications to serve AI/ML models, including Hugging Face Transformers and other pre-trained architectures. Build, train, and evaluate ML models using services such as AWS SageMaker, Bedrock, Glue, Athena, Redshift … in production Hands-on experience with AWS Machine Learning and Data services: SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, and Redshift. Familiarity with deploying Hugging Face models (e.g., NLP, vision, and generative models) within AWS environments. Ability to develop and host microservices and REST APIs using Flask, FastAPI More ❯

efficient data processing, feature engineering, and data transformation tailored for LLM and GenAI applications. GenAI Application Development: Apply and integrate frameworks like LangChain and Hugging Face Transformers to build modular, scalable, and robust Generative AI data pipelines and applications. Prompt Engineering Application: Apply advanced prompt engineering techniques to … computer science, or a related field. Programming and Frameworks: Hands-on experience with Python, TensorFlow or PyTorch , and NLP libraries such as spaCy and Hugging Face. GenAI Tools: Practical experience with LangChain , Hugging Face Transformers, and embedding models for building GenAI applications. Prompt Engineering: Expertise in prompt More ❯

degree in Computer Science, AI, Data Science, or a related field. 3+ years of experience in AI/ML development. Generative AI experience with Hugging Face, fine-tuning open-source LLMs like Mistral/Llama/Gemma/Phi/Qwen, vLLM, Text Generation Inference, unsloth, LoRA, adapters … DPO, ORPO, hugging face inference endpoints, LlamaIndex Experience with embeddings, vector databases, and deep learning models. Hands-on experience with cloud AI services (AWS, GCP, Azure) and developing microservices using FastAPI, Flask, or Django, with expertise in containerized deployment (Docker) Knowledge of software engineering principles and best practices More ❯

functional team of ML engineers, data scientists, and product managers to build robust and scalable ML systems. Our team relies on tools like PyTorch, Hugging Face, and internal infrastructure for model training, deployment, and experimentation. Requirements Strong understanding of machine learning fundamentals and the practical trade-offs involved … deep learning and NLP Experience with Pytorch or other deep learning frameworks Hands-on experience fine-tuning LLMs or working with frameworks such as Hugging Face Transformers Proven experience deploying ML models to production and maintaining end-to-end ML pipelines Solid communication skills - in person, in writing More ❯

Machine Learning Engineer - SaaS - London (Tech stack: Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine Learning, Google Cloud AI … will be provided to fill any gaps in your skill set): Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine More ❯

on platform operations or service management Hands-on experience with managing GenAI/ML platforms and LLM-based services (e.g., OpenAI, Anthropic, Azure OpenAI, Hugging Face) Proven track record in implementing and scaling MLOps or LLMOps practices in a production environment Certifications in cloud platforms (e.g., Azure, AWS More ❯

experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers , or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience More ❯

experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience More ❯

deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on More ❯

Vertex AI (Workbench, Training, Prediction, Pipelines, Feature Store). Proficiency in Python and strong experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX, Hugging Face Transformers). Experience with data engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow). More ❯

Qualifications: Experience: 3-5 years of experience in AI/ML development, with a focus on working with Large Language Models (e.g., GPT, BERT, Hugging Face, etc.). Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent More ❯

Qualifications: Experience: 3-5 years of experience in AI/ML development, with a focus on working with Large Language Models (e.g., GPT, BERT, Hugging Face, etc.). Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent More ❯

Data Science, or a related quantitative field. Proficiency in Python, including hands-on experience with machine learning libraries and frameworks (e.g., Pandas, Pytorch, Tensorflow, HuggingFace). Demonstrated experience in data science and machine learning through projects, internships, or prior roles, with exposure to or hands on experience with Gen AI More ❯

Vertex AI (Workbench, Training, Prediction, Pipelines, Feature Store). • Proficiency in Python and strong experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX, Hugging Face Transformers). • Experience with data engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow). More ❯

Vertex AI (Workbench, Training, Prediction, Pipelines, Feature Store). • Proficiency in Python and strong experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX, Hugging Face Transformers). • Experience with data engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow). More ❯

Vertex AI (Workbench, Training, Prediction, Pipelines, Feature Store). • Proficiency in Python and strong experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX, Hugging Face Transformers). • Experience with data engineering and building robust data pipelines for AI/ML workloads on GCP (e.g., BigQuery, Dataflow). More ❯

scale vector stores for embeddings. Author best practices on the Generative AI ecosystem, when to use which tools, available models such as GPT, Llama, Hugging Face etc. and libraries such as Langchain. Analyze, investigate, and implement GenAI solutions focusing on Agentic Orchestration and Agent Builder frameworks. Contribute to More ❯

database technologies and Semantic Search techniques & tools. Proficiency in programming languages such as Python and experience with NLP libraries such as NLTK, spaCy, Transformers, Hugging Face, BERT and Gen AI frameworks like Llangchain. Llamaindex etc. Strong problem-solving skills and ability to handle complex NLP challenges. Excellent communication More ❯

database technologies and Semantic Search techniques & tools. Proficiency in programming languages such as Python and experience with NLP libraries such as NLTK, spaCy, Transformers, Hugging Face, BERT and Gen AI frameworks like Llangchain. Llamaindex etc. Strong problem-solving skills and ability to handle complex NLP challenges. Excellent communication More ❯

database technologies and Semantic Search techniques & tools. Proficiency in programming languages such as Python and experience with NLP libraries such as NLTK, spaCy, Transformers, Hugging Face, BERT and Gen AI frameworks like Llangchain. Llamaindex etc. Strong problem-solving skills and ability to handle complex NLP challenges. Excellent communication More ❯

MongoDB Experience on general programming languages: Python, C++, Java, etc. Experience with deep learning, machine learning and NLP frameworks such as PyTorch (or TensorFlow), HuggingFace Transformer, Scikit-learn Experience with working in Linux A strong intuition for what makes products a joy to use Empathy for how different users will More ❯

performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, Hugging Face, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases More ❯

and data governance, and at least 2 years of experience with cloud-based AI/ML technologies (such as tools from AWS, Azure, Google, HuggingFace, OpenAI and Databricks) building ML or applied AI solutions. • A passion for Generative AI, and an understanding of strengths and weaknesses of Generative LLM's More ❯

Linguistics, or related fields (MSc/PhD preferred). Experience with computational linguistics is highly desirable. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, Scikit-learn. Experience deploying AI solutions using AWS, GCP, or Azure. Hands-on experience with MLOps, CI/CD, and More ❯