1 to 25 of 82 Hugging Face Jobs in England

Data Science and DevOps teams, ensuring effective AI/ML solution deployment Hands-On Support - Assist data scientists with DevOps issues, Docker containers, and MLOps tooling Model Deployment - Deploy Hugging Face Transformers and ML models as secure microservices AWS ML Platform - Build and evaluate models using SageMaker, Bedrock, Glue, Athena, and Redshift Knowledge Transfer - Create documentation and mentor … engineering, model evaluation Critical Technical Skills: Production ML deployment - Demonstrated experience maintaining AI/ML models in production AWS ML services - SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, Redshift Hugging Face deployment - NLP, vision, and generative models in AWS environments API development - Flask, FastAPI microservices and REST API frameworks DevOps integration - CI/CD pipelines, Jenkins, Maven, Chef … Working across cloud-based infrastructures Tech Stack & Tools AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch Development: Python, R, Flask, FastAPI, SQL MLOps: Apigee, Hugging Face, Jenkins, Git, Docker Environments: Jupyter, RStudio, Linux What We're Looking For Experience Level: Sr. Associate or Manager with hands-on data analytics and software delivery experience More ❯

for data quality, lineage, privacy, and security, ensuring our AI systems are developed and used responsibly and ethically. Tooling the Future: Get hands-on with cutting-edge technologies like Hugging Face, PyTorch, TensorFlow, Apache Spark, Apache Airflow, and other modern data and ML frameworks. Collaborate and Lead: Partner closely with ML Engineers, Data Scientists, and Researchers to understand … Data Governance & Ethics: Experience implementing data governance frameworks, ensuring data quality, privacy, and compliance, with an awareness of ethical AI considerations. Bonus Points If You Have: Direct experience with Hugging Face ecosystem, PyTorch, or TensorFlow for data preparation in an ML context. Experience with real-time data streaming architectures. Familiarity with containerization (Docker, Kubernetes). Master's or More ❯

Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Key Skills & Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline orchestration in AWS. OCR … Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging Face Transformers. Writing clean, modular, and testable code. Traditional Machine Learning Models: Experience with regression (linear, ridge), classification (logistic regression, decision trees, random forests), clustering (k-means, DBSCAN More ❯

our product and community. Engineering Design, develop, and optimize AI/ML features in Qdrant's core engine and SDKs. Prototype and implement integrations with popular ML frameworks (e.g., Hugging Face, OpenAI, LangChain). Analyze performance, identify bottlenecks, and implement scalable solutions in real-world AI pipelines. Collaborate with product and engineering teams to define and deliver impactful … inform product development. Requirements Strong proficiency in Python. Solid understanding of machine learning concepts, embeddings, and vector search. Experience with at least one modern ML framework (e.g., PyTorch, TensorFlow, Hugging Face). Excellent communication skills; ability to explain technical topics to diverse audiences. Prior experience contributing to open-source projects or engaging with developer communities. Comfortable presenting and 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 ❯

AI. Able to contextualise these techniques in industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be … essential. Staying updated on the latest trends and research is important. Motivation Techniques - Being able to motivate and inspire learners to stay committed to their programme, even in the face of challenges. Self-awareness - Ability to understand your own biases, values, and beliefs, which can impact your coaching approach. Time Management - Effective time management skills are essential for structuring More ❯

applications. Proficiency in Python and common ML/AI libraries; comfortable with full-stack system design from prototyping to production deployment. Familiarity with one or more LLM stacks (OpenAI, Hugging Face Transformers, LangChain etc) Experience with model serving frameworks and scalable deployment strategies Experience with containerisation and orchestration "Nice To Have" Skills and Experience : Exposure to hardware design … at Arm Arm's approach to hybrid working is designed to create a working environment that supports both high performance and personal wellbeing. We believe in bringing people together face to face to enable us to work at pace, whilst recognizing the value of flexibility. Within that framework, we empower groups/teams to determine their own hybrid 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 ❯

principles and version control (Git) Experience working in cloud environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with Hugging Face Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g. MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI More ❯

resolve complex technical challenges in big data and ML systems. About You You're fluent in Python and experienced with leading ML/AI frameworks (PyTorch, TensorFlow, Scikit-learn, Hugging Face). You have a solid background in MLOps: experiment tracking, CI/CD for ML, model versioning, deployment, and monitoring. You've built scalable backend systems and More ❯

deployment tools (e.g., Docker, Kubernetes). Excellent problem-solving skills and the ability to work on complex AI challenges. Nice to Have: Experience with NLP frameworks, transformer architectures, and Hugging Face libraries. Knowledge of AI ethics, data privacy, and model interpretability. Previous experience working on AI-based translation, automation, or localization projects. Roadmap for Success First 90 Days 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

Required skills: Solid foundation in AI/ML principles (e.g. neural networks, NLP, transformers, speech processing). Hands-on experience with Python and popular ML frameworks (e.g. PyTorch, TensorFlow, Hugging Face). Familiarity with LLM APIs and open-source models (e.g. GPT, Claude, Mistral, Whisper). Familiarity with agentic AI frameworks (LangChain, AutoGen, CrewAI, OpenAI Functions). Interest More ❯

AI researchers, and data scientists, driving end-to-end AI solution delivery. Designing and implementing AI architectures across cloud and on-prem environments, leveraging tools like PyTorch, TensorFlow, and Hugging Face. Spearheading the development of production ready AI models, from foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI pipelines and More ❯

and implemention synchronous, asynchronous and batch data processing operations Expert level programming skills in Python, along with experience in using relevant tools and frameworks such as PyTorch, FastAPI and Huggingface; strong programming skills in Java are a plus Expert level know-how of ML Ops systems, data pipeline design and implementation, and working with ML platforms (preferably AWS SageMaker) Strong More ❯

how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using Cloud technologies eg. AWS, GCP or More ❯

learning modelling techniquesand how to fine-tunethosemodelseg.XGBoost, Deep Neural Networks, Transformers,ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning librarieseg.Fastai,Keras,Tensorflow,pytorch, sci-kit learn,huggingface,etc. Must demonstrate the capacity of reading, understanding and implementingnew techniques in the field of machine learning as they emerge. Experience of using Cloud technologieseg.AWS, GCP or Azure Specialisedvisualisationtechniqueseg.D3.js,ggplotetc. 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 ❯

big data engineering to productionizing classical ML and LLMs, you'll be at the core of AI infrastructure. Key Requirements: Expert in Python, ML/AI frameworks (PyTorch, TensorFlow, Hugging Face) Proven MLOps, big data, and backend/API development experience Deep understanding of NLP and LLMs Proficient with cloud platforms (AWS/GCP/Azure), Airflow, DBT More ❯

big data engineering to productionizing classical ML and LLMs, you'll be at the core of AI infrastructure. Key Requirements: Expert in Python, ML/AI frameworks (PyTorch, TensorFlow, Hugging Face) Proven MLOps, big data, and backend/API development experience Deep understanding of NLP and LLMs Proficient with cloud platforms (AWS/GCP/Azure), Airflow, DBT More ❯