1 to 25 of 121 Permanent PyTorch Jobs in London

implementation experience NLP and Conversational AI development Model training, testing and evaluation Prompt Engineering and AI workflow optimisation Programming & Frameworks Strong Python development skills PyTorch TensorFlow Scikit-Learn LangChain LangGraph Hugging Face FastAPI REST APIs Data Technologies SQL NoSQL Databases Data Modelling ETL Development Data Pipeline Engineering Vector Databases such ...

Bring Technical Expertise: Strong Python skills with the ML stack: NumPy, pandas, scikit-learn Hands-on experience with at least one deep learning framework (PyTorch, TensorFlow, JAX) Solid grounding in core ML—supervised, unsupervised, evaluation, regularisation, statistics Production ML experience—deploying, serving and monitoring models at scale LLM and generative ...

developing junior members of a team and wider community. \n \n\n DESIRABLE: \n \n \nDemonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g, GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). \n \n\n \n \nExperience with ...

environments. \n Experience with MLOps practices and tools for managing the machine learning lifecycle. \n Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Strong understanding of data preprocessing, feature engineering, and evaluation techniques. \n Experience working with large, complex datasets and applying statistical ...

environments. \n Experience with MLOps practices and tools for managing the machine learning lifecycle. \n Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Strong understanding of data preprocessing, feature engineering, and evaluation techniques. \n Experience working with large, complex datasets and applying statistical ...

scale production environments.\ NExperience with MLOps practices and tools for managing the machine learning lifecycle.\ NProficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn.\ NStrong understandingof data preprocessing, feature engineering, and evaluation techniques.\ NExperience working with large, complex datasets and applying statistical analysis.\ NFamiliarity with ...

large scale production environments.\ N\n\nExperience managing data science teams and couching team members\ N\n\nProficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).\ N\n\nHands-onexperience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK).\ N\n\nExperience with generative models (transformers, GANs/ ...

managing, coaching, and developing junior members of a team and wider community.\ N\n\nDESIRABLE: \ N\n\nDemonstrableexperience with modern deep learning frameworks (e.G. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.G, GPT, Llama, Claude, Gemini), machine learning libraries (e.G. scikit-learn, XGBoost).\ N\n\n\n\ NExperience ...

using LangChain and LangGraph (agent architecture design, state management, and graph-based workflow orchestration). ML & Cloud Ecosystems: Solid understanding of ML algorithms, FastAPI, PyTorch/TensorFlow, MLflow, MLOps practices, containerization (Docker, Kubernetes/AWS EKS), and cloud services (AWS Bedrock, SageMaker, Azure AI Search). Soft Skills: Excellent communication ...

Microsoft Azure) \n Knowledge of containerisation technologies like Docker and orchestration tools such as Kubernetes \n Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, or Java-based ML libraries) \n Strong problem-solving and communication skills \n\n Preferred Qualifications \n \n Experience with NLP, computer vision, or recommendation ...

Microsoft Azure) \n Knowledge of containerisation technologies like Docker and orchestration tools such as Kubernetes \n Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, or Java-based ML libraries) \n Strong problem-solving and communication skills \n\n Preferred Qualifications \n \n Experience with NLP, computer vision, or recommendation ...

adoption and ensure clients receive the most relevant, timely, and actionable content. You will utilize technologies including Python (Pandas, Polars, Scikit‐learn, TensorFlow/PyTorch), Jupyter, Trino, SQL, and gain exposure to graph database technologies. RESPONSIBILITIES AND QUALIFICATIONS Responsibilities: Design, build, and maintain robust data pipelines for feature engineering ...

engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud ...

engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \n\n \n Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \n\n Platform & Cloud Engineering: \n \n Architecture and delivery of scalable cloud ...

adoption and ensure clients receive the most relevant, timely, and actionable content. You will utilize technologies including Python (Pandas, Polars, Scikit‐learn, TensorFlow/PyTorch), Jupyter, Trino, SQL, and gain exposure to graph database technologies. RESPONSIBILITIES Design, build, and maintain robust data pipelines for feature engineering and model training, ensuring ...

retrieval‐augmented generation, tool/function calling, agentic workflows), including evaluation and safety patterns. Expertise with machine learning frameworks and data science packages (e.g., PyTorch, TensorFlow, Scikit‐Learn, NumPy, Pandas, SciPy, statsmodels). Experience deploying to AWS (e.g., SageMaker, Bedrock) and operating production workloads with attention to cost, performance, security ...

MLOps tooling - for versioning, reproducibility, observability etc. \n Experience monitoring, maintaining, enhancing existing models over an extended time period \n Extensive experience with pytorch and related data science python libraries (e.g. pandas) \n Experience of containerising applications or models for deployment (Docker) \n Experience with one of the major public ...

MLOps tooling - for versioning, reproducibility, observability etc. \n Experience monitoring, maintaining, enhancing existing models over an extended time period \n Extensive experience with pytorch and related data science python libraries (e.g. pandas) \n Experience of containerising applications or models for deployment (Docker) \n Experience with one of the major public ...

Platform, or Microsoft Azure)\ NKnowledge of containerisation technologies like Docker and orchestration tools such as Kubernetes\ NExperience with AI/ML frameworks (e.G., TensorFlow, PyTorch, or Java-based ML libraries)\ NStrong problem-solving and communication skills\ N\nPreferred Qualifications\ N\nExperience with NLP, computer vision, or recommendation systems\ NFamiliarity with ...

Science, Machine Learning, or related field. \n Experience building and deploying agentic AI applications in production environments. \n Expertise with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Proficiency in Python; experience writing comprehensive tests and building evaluation harnesses for agents and prompts. \n Hands-on experience with ...

Science, Machine Learning, or related field. \n Experience building and deploying agentic AI applications in production environments. \n Expertise with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. \n Proficiency in Python; experience writing comprehensive tests and building evaluation harnesses for agents and prompts. \n Hands-on experience with ...

frameworks. Strong proficiency in programming languages such as Python, Java, or Go, along with experience integrating with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch). Proven ability to translate complex business requirements into well‐defined, cloud‐optimized application architectures, scalable data models (e.g., relational, NoSQL, graph), and technical specifications ...

feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. \ N\n\nPractical knowledge of ML frameworks (e.G. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). \ N\nPlatform & Cloud Engineering: \ N\nArchitecture and delivery of scalable cloud data ...

Track record of leading ML/AI technical initiatives across multiple teams Strong experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality ...

approaches for standardised integration between AI applications, tools, and enterprise data sources Strong Python engineering skills, including experience with libraries and frameworks such as PyTorch, Pydantic, LangChain, and LangSmith Experience with prompt orchestration, structured outputs, evaluation, and tool-calling patterns for LLM applications Knowledge of RAG patterns, vector search ...