1 to 25 of 133 Hugging Face Jobs in London

preprocessing, and feature engineering. Proven 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 with fine-tuning and distillation More ❯

on expertise building and deploying deep learning models (eg, 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 cloud platforms (AWS SageMaker, GCP More ❯

on expertise building and deploying 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 cloud platforms (AWS SageMaker, GCP More ❯

and scalable AI solutions. Requirements 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 experience). Technical Skills: Proficiency More ❯

and scalable AI solutions. Requirements 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 experience). Technical Skills: Proficiency More ❯

responses to ensure accuracy and relevance. • Deep understanding of AI/ML algorithms, LLMs, and modern AI architectures. • Proficiency in Python, SQL and AI/ML frameworks such as Hugging Face, LangChain, scikit-learn, TensorFlow, PyTorch and Semantic Kernel etc. • Develop compelling data visualizations and dashboards to support storytelling and real-time insights. • Ensure compliance with data governance More ❯

and Data cloud solutions. Experience working with Azure AI services (or AWS, GCP) and AI model deployment. Understanding of generative AI, LLMs, NLP, and machine learning frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Understanding of the AI application lifecycle, including application prototyping, productionising and monitoring. Python/Go/R. Problem solving skills. Strong written and verbal communication More ❯

and Data cloud solutions. Experience working with Azure AI services (or AWS, GCP) and AI model deployment. Understanding of generative AI, LLMs, NLP, and machine learning frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Understanding of the AI application lifecycle, including application prototyping, productionising and monitoring. Python/Go/R. Problem solving skills. Strong written and verbal communication More ❯

data lake architectures, data integration, 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 • Fundamental knowledge of ML, and More ❯

AI/ML services, especially 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). • Solid understanding of core AI More ❯

AI/ML services, especially 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). Solid understanding of core AI More ❯

Gemini, Llama, Falcon, Mistral. Model 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 and data platforms: SQL, NoSQL More ❯

love to hear from you! Requirements Proficiency in at least one programming language (e.g., Python, C#, JavaScript, Kotlin, Java, Go). Experience with Gen AI tools (e.g., LangGraph, CrewAI, Hugging Face, OpenAI APIs) and ML frameworks such as PyTorch/TensorFlow. Experience in building and optimising systems for performance, scalability, and reliability. Strong understanding of ML fundamentals (training More ❯

running a large-scale cloud-native Machine Learning platform Extensive experience with programming languages such as Python, Java, Scala etc. Solid experience with ML frameworks such as Pytorch and Huggingface The ability to work in a team, collaborate with others to solve interesting problems that directly affect our customers Demonstrated critical thinking and problem-solving abilities, excellent communication and written More ❯

D problems that directly impact customers. OUR TECHNOLOGY STACK: Python • Agentic tools (Autogen, Semantic Kernel, LangChain) • SQL • MongoDB • Third-party LLM APIs • LiteLLM • MLflow • Google Cloud Platform • Docker • PyTorch • Hugging Face Transformers • SPARQL • Kubernetes WHAT YOU WILL BE DOING AT BEAMERY Join us at the forefront of transforming how organisations manage talent and plan their workforce. We're More ❯

engineering. Demonstrated experience working closely with business stakeholders in R&D, translating complex scientific challenges into AI solutions. Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Hugging Face). Experience building and deploying models in cloud environments (e.g. Azure, GCP). Solid understanding of statistics, optimization, and experimental design. Preferred Qualifications & Skills: Please note the More ❯

LLM such as GPT, LLaMA, Claude, or similar, including fine-tuning, prompt engineering, and deploying models in production environments. Strong proficiency in Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenisation, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG More ❯

pipelines Working knowledge of embedding compliance and security in ML systems, including governance, access controls, and regulatory alignment (e.g., GDPR, HIPAA) Proficient with modern AI tooling and ecosystems, including Hugging Face, Cursor, vector DBs, and productivity tools that accelerate GenAI development Expertise in GenAI and LLMs, with hands-on experience in RAG solutions and agentic frameworks; capable of More ❯

platforms such as AWS, GCP, or Azure Understanding of API integration and deploying solutions in cloud environments Familiarity or hands-on exposure to generative AI ecosystems (e.g., OpenAI, Bedrock, Hugging Face) LLMs & Emerging Tech Awareness Awareness of large language models (LLMs) and a strong enthusiasm for staying current with advancements in generative AI and applied machine learning Communication More ❯

video) • Familiar with Machine Learning (ML) models like XGBoost, deep neural networks, transformers, Markov chains • Experienced in using Machine Learning (ML) libraries such as scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face Cloud & Deployment • Hands-on experience with AWS, Azure, or GCP • Understanding of API integration and cloud-based Machine Learning (ML)/AI deployment Research & Innovation • Ability to More ❯

define priorities and influence the product roadmap What we look for: Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade More ❯

with a strong emphasis on code quality and reliability through comprehensive testing. Extensive experience with generative AI models, both as cloud service APIs (e.g., OpenAI) and open source (e.g., Huggingface). Experience with integrating user feedback to establish data flywheels and self-improving AI applications. Solid understanding of data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms 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 ❯

of RAG techniques and vector search implementation. Experience in designing and deploying AI agents . Proficiency in Python and its AI/ML-related libraries (e.g., TensorFlow, PyTorch, LangChain, Hugging Face, FastAPI). Experience with Vector Databases (e.g., Pinecone, FAISS, Weaviate) and GraphQL (preferred). Familiarity with MLOps practices, CI/CD for AI models, and cloud-based More ❯

of RAG techniques and vector search implementation. Experience in designing and deploying AI agents . Proficiency in Python and its AI/ML-related libraries (e.g., TensorFlow, PyTorch, LangChain, Hugging Face, FastAPI). Experience with Vector Databases (e.g., Pinecone, FAISS, Weaviate) and GraphQL (preferred). Familiarity with MLOps practices, CI/CD for AI models, and cloud-based More ❯