25 of 25 Remote Hugging Face Jobs in the UK

/ML solutions. Responsibilities Collaborate across Data Science and DevOps to turn experimental ML models into deployed applications. Package and deploy ML models (including Hugging Face Transformers) as microservices on AWS. Build, train, and evaluate models using AWS tools like SageMaker, Bedrock, Glue, Athena, and Redshift. Develop secure … mindset. Enthusiastic about learning, adapting to new tools, and collaborating in a dynamic environment Tech Stack Languages & Frameworks: Python, R, SQL, Flask, FastAPI Tools: Hugging Face, Apigee, Jenkins, Git, RStudio, Jupyter Cloud: AWS (SageMaker, Glue, Athena, Redshift, Bedrock, ECS Fargate, Lambda) Please apply online for consideration. 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 ❯

Product, Engineering, and Customer Success to align AI-powered features with user needs. Evaluate, fine-tune, and integrate third-party AI models (e.g., OpenAI, Hugging Face) into our SaaS platform. Define best practices and workflows for integrating AI into a high-growth SaaS product. Contribute to the design … working with LLMs, NLP, and ML pipelines to power user-facing features. Strong Python skills and experience with frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with LLM fine-tuning, embeddings, and retrieval-augmented generation (RAG). Practical experience deploying AI models into production environments and More ❯

applied solutions. Required Skills & Experience: Strong background in machine learning , NLP , and deep learning . Hands-on experience with LLM frameworks and APIs (OpenAI, HuggingFace Transformers, LangChain, LlamaIndex). Experience building RAG pipelines with tools such as FAISS , Weaviate , Qdrant , or Pinecone . Solid knowledge of graph databases (Neo4j, TigerGraph More ❯

hands-on experience in either academia or industry. Proficiency in Python and experience with ML/NLP libraries like TensorFlow, PyTorch, scikit-learn, or Hugging Face. Experience working with cloud platforms such as AWS, GCP, or Azure. Familiarity with advanced NLP methods, including Prompt Engineering, Parameter-Efficient Fine-Tuning More ❯

with exposure to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps More ❯

with exposure to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps More ❯

/ML systems in production. Strong background in LLMs, NLP, and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization 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 ❯

ML initiatives. Expertise in LLM-driven architectures, prompt engineering, and fine-tuning. Strong hands-on experience with AI frameworks like LangChain, OpenAI APIs, and Hugging Face. Deep understanding of multi-agent systems, decision-making algorithms, and reinforcement learning. Experience with cloud-based AI deployment (AWS, GCP, or Azure). More ❯

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 More ❯

NLP applications. Technical Skills: Knowledge of tools like Dialogflow, Microsoft Bot Framework, Rasa, and Amazon Lex. Knowledge with libraries such as spaCy, NLTK, and Hugging Face Transformers. Practical experience with Python, REST APIs, and dialog model training data preparation. Excellent ability to present complex AI concepts clearly and More ❯

AI models for real-world implementation. Candidate Requirements Strong Python programming skills, with experience in AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face. Extensive experience in NLP and Generative AI , including working with large-scale language models. Proficiency in AWS cloud computing , including Athena, SageMaker, and More ❯

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 More ❯

participate in the upside of an ultra-growth venture. Have fun. Apply if: You have experience with our stack: Python, LlamaIndex/LangChain, PyTorch, HuggingFace, FastAPI, Postgres, SQLAlchemy, Alembic, OpenAI, Docker, Azure, Typescript, React. You have hands-on experience working with LLMs, RAG architectures, NLP, or applying Machine Learning to More ❯

skills in algorithm development to solve optimisation problems Familiarity with core tools for a typical ML-focused work environment (e.g. Git, IDE, common libraries, HuggingFace). Ability to work as part of a team Be comfortable with a fast-paced, high-risk, and a high reward environment Desirable skills Experience More ❯

experience in symbolic AI techniques, knowledge representation, and rule-based systems. Experience with large language models (LLMs) and associated tooling( for example OpenAI, Anthropic, Huggingface), and a solid grasp of natural language processing techniques to enhance machine understanding and interaction. Knowledge of vector databases, embeddings, and retrieval-augmented generation style More ❯

cost, or improving our search service Tech Stack: Platform: AWS, Pulumi, Docker, Prefect, Github actions, Grafana cloud monitoring Data Science: Python, PyTorch, Pandas, Spacy, Huggingface, Numpy, Streamlit, Argilla, Weights and Biases Backend: Python, FastAPI, PostgreSQL, Vespa, SQLAlchemy, AWS Batch, Lambda, S3 Frontend: React, Next.js Key skills and experience: Experience using More ❯

of NLP models. • Build custom language models tailored to specific industry requirements. What We're Looking For: • Proven experience with NLP frameworks such as Hugging Face, spaCy, or TensorFlow. • Hands-on experience working with Large Language Models (LLMs) and deploying them in production. • Advanced Python skills, with the More ❯

RAG Expertise: Experience in Retrieval-Augmented Generation and related vector databases (e.g., Pinecone, FAISS, Weaviate). LLMs & NLP: Experience working with LLMs (OpenAI, Anthropic, Hugging Face, etc.), including model tuning, security, and optimization. Regulatory Knowledge: Understanding of AI governance, compliance frameworks, and security best practices. .NET Experience: Familiarity More ❯

equivalent experience in ML, NLP, or a related discipline, with strong understanding of LLM internals. Toolbelt Ready : Proficient in Python and LLM tools like Hugging Face, LangChain, or OpenAI API. Prompt-Savvy : Comfortable with prompt engineering, model evaluation, and structured analysis of generative systems. Experimentation First : Experience (or More ❯

accurate, safe, and clinically relevant. Key responsibilities . Curate, label and augment real chat data to fine-tune a large-language-model (Azure OpenAI, Hugging Face, etc.) . Design and run evaluation experiments (precision/recall, BLEU, CSAT, etc.), then turn findings into actionable model tweaks. . Optimise More ❯

Experience building and integrating LLM-powered systems, including prompt engineering, embedding models, and custom RAG pipelines Familiar with modern AI toolkits such as LangGraph, Hugging Face Transformers, Vector Database, OpenAI APIs, and similar Comfortable managing backend, frontend, and infrastructure with minimal oversight - you know how to run a More ❯

marketing to AI labs, GPU buyers, DevOps teams, or enterprise R&D Familiarity with cloud-native technologies, LLM infrastructure, or ecosystem players like NVIDIA, Hugging Face, or MLOps vendors Background in partner or channel marketing Working knowledge of SEO, SEM, and campaign analytics Benefits Competitive total compensation package More ❯

At Appwrite we're looking for a AI Engineer to join our team. About the job We're looking for a passionate and curious AI Engineer to join our team and help us explore, prototype, and implement AI-driven features More ❯