1 to 25 of 96 Hugging Face Jobs

processing tools and platforms (e.g., SQL, Apache Spark, Hadoop). Knowledge of cloud computing services (e.g., AWS, Google Cloud, Azure) and containerization technologies (e.g., Docker, Kubernetes) is a plus. Hugging Face Ecosystem: Demonstrated experience using Hugging Face tools such as the Transformers library, Datasets, and Tokenizers. Familiarity with integrating pre-trained models and fine-tuning them 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 ❯

ML and/or GenAI projects end-to-end Proficient with Python and common data science tools and libraries Hands-on experience with GenAI tools and frameworks such as Hugging Face, LangChain, and open-source LLMs Familiar with cloud platforms (Azure preferred), and tools such as Databricks Comfortable using cloud platforms (especially Azure), and familiar with tools such … as Databricks, Hugging Face, LangChain, and open-source GenAI libraries Comfortable working with clients and explaining technical concepts to non-technical stakeholders A natural problem solver, with a keen interest in learning and staying ahead of developments in the AI space Ideally experienced working in a consultancy or client-facing role If you're passionate about data, and 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 ❯

programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning algorithms (supervised, unsupervised, deep learning), including model evaluation, explainability, and selection for business More ❯

to tools like KServe, Ray Serve, Triton, or vLLM is a big plus Bonus Points Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure or GCP Passion for financial services Qualifications Degree in Computer Science, Engineering, Data Science, or similar What We Offer A collaborative and innovative work environment with excellent 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 ❯

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 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 ❯

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 ❯

Applied Math, or a related technical field. 3+ years of experience developing and deploying machine learning models. Deep proficiency in Python and major AI/ML libraries (e.g., PyTorch, HuggingFace Transformers, scikit-learn). Strong experience with end-to-end MLOps and model deployment in containerized environments. Familiarity with security-first software development practices (e.g., static code analysis, SBOMs, STIGs 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 ❯

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 ❯