1 to 25 of 425 Permanent PyTorch Jobs in London

Machine Learning Deep Learning Neural Networks Natural Language Processing (NLP) Generative AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Reinforcement Learning Frameworks & Technologies PyTorch TensorFlow Scikit-learn LangChain Hugging Face OpenAI and Azure OpenAI Services Vector Databases Docker Kubernetes Cloud Platforms Microsoft Azure Azure AI Services Azure Machine Learning ...

assistants/agents).Strong proficiency in Python for AI/ML development, including use of common ML and data libraries (e.g. TensorFlow/PyTorch, scikit-learn, pandas, NumPy).Proven experience integrating AI services with enterprise systems via APIs and event-driven architectures.Practical experience with LLMs, prompt engineering, RAG and tools ...

assistants/agents). Strong proficiency in Python for AI/ML development, including use of common ML and data libraries (e.g. TensorFlow/PyTorch, scikit-learn, pandas, NumPy). Proven experience integrating AI services with enterprise systems via APIs and event-driven architectures. Practical experience with LLMs, prompt engineering ...

levels) Requirements Skills and Qualifications Essential Skills: Strong Python/R (pandas/NumPy; ML libs such as scikit-learn; DL frameworks TensorFlow/PyTorch). Experience with LLM/RAG toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search). Hands ...

quality assessment. Good foundation in statistics, probability, optimization, experimentation, and applied machine learning. Advanced proficiency in Python and modern AI frameworks, including PyTorch, TensorFlow, Scikit‐Learn, Hugging Face, LangChain, Semantic Kernel, and related ecosystems. Experience building and scaling enterprise AI platforms, ML infrastructure, and intelligent applications serving thousands of users. ...

sized for training and checkpointing workloads. AI Software, MLOps & Generative AIOrchestration & containers: Kubernetes, Docker, Slurm, Run:ai or equivalent GPU scheduling platforms. ML frameworks: PyTorch and TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM ...

Skills & Experience: Experience in machine learning, data science, or applied AI roles. Proficiency in Python and ML libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Strong understanding of statistics, ML algorithms, and model evaluation techniques. Experience working with cloud platforms (AWS, Azure, or GCP) and ML pipelines. Ability to analyse ...

Language Model (LLM) powered solutions and generative AI applications at enterprise scale. Strong Python development skills, with experience using machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn. Deep understanding of AI Engineering, MLOps practices, and modern LLM technologies, including prompt engineering, Retrieval-Augmented Generation (RAG), AI agents ...

engineering and building integrated solutions Strong articulation, stakeholder management and consulting skills Experience driving productivity and cost optimisationsSkills: Python with AI/ML frameworks (PyTorch, TensorFlow). Prompt Engineering, Langchain,Langsmith/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex ...

learning and deep learning, time series forecasting, reinforcement learning, optimization, or conversational AI. Familiarity with Python and common AI/ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn, Pandas, NumPy). Software engineering fundamentals — version control, code review, modular design, and writing testable code intended for someone else ...

lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms on AWS, Azure ...

with clean, maintainable codeSolid understanding of ML fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metricsExperience with PyTorch or TensorFlowGenAI specificHands on experience with LLM APIs or open source models such as Llama or MistralExperience building RAG systems with vector databases such as FAISS, Pinecone ...

Jira, GitHub, and GitLab. Hands-on experience building, deploying, and scaling AI/ML solutions using frameworks and platforms such as Scikit-learn, PyTorch, TensorFlow, Hugging Face, LangChain, Databricks, Spark, vector databases, cloud services (AWS/Azure), and MLOps tools including MLflow and Kubeflow. Proven ability to develop production-grade ...

parallel and high-throughput file systems (e.g. Everpure, WEKA, VAST, NetApp) sized for training and checkpointing workloads. AI Software, MLOps & Generative AI ML frameworks: PyTorch and TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM ...

deploying AI applications in large scale production environments. Experience managing data science teams and couching team members Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn). Hands-on experience with agentic frameworks (LangChain, CrewAI, AutoGen, LangGraph, ADK). Experience with generative models (transformers, GANs/VAEs; diffusion models ...

bring 5+ years of experience in AI, Machine Learning, Data Science, or related fields. Strong Python skills and experience with frameworks such as PyTorch, TensorFlow, and Hugging Face. Experience taking AI proof‐of‐concepts from idea to production. Strong knowledge of deep learning, NLP, and LLM architectures and experience ...

frameworks such as LangChain/LangGraph or Hugging Face. Strong Python and SQL, with working knowledge of the wider ML stack (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost). Experience with cloud data and ML platforms, ideally AWS (S3, SageMaker Pipelines, Glue) and/or GCP, and MLOps practices (MLflow ...

experience with classical NLP techniques (BERT, topic modelling, summarisation), statistical analysis, and knowledge graphs. Deep Learning: Familiarity or experience with Deep Learning (TensorFlow, PyTorch). What We Offer: Competitive Salary Bonus Scheme Private Healthcare Insurance 25 Days Annual Leave + Bank Holidays Up to 10 days allocated for development training ...

learning, statistical modelling, experimental design and model validation. Proficiency in Python for data science and machine learning, including libraries such as 'pandas', 'scikit-learn', 'PyTorch' or 'TensorFlow'. Experience cleaning, integrating and preparing complex or heterogeneous datasets for analysis. Familiarity with software engineering best practices, including Git, testing, documentation ...

Machine Learning Deep Learning Neural Networks Natural Language Processing (NLP) Generative AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Reinforcement Learning Frameworks & Technologies PyTorch TensorFlow #J-18808-Ljbffr ...

concept through to production. Strong technical knowledge, including proficiency in Python and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow). Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing. Familiarity with Generative AI concepts ...

have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology ...

have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology ...

have experience in many of the following: Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology ...

functional programming and JVM based languages- Python, Java Experience integrating models into cloud scale, microservices based architectures Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers Hands-on experience with AWS services, and Databricks Experience/Exposure to SQL, NoSQL and messaging stacks Excellent verbal & written ...