1 to 25 of 120 Permanent PyTorch Jobs in London

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 ...

with frameworks such as NumPy, Pandas, Scikit-learn, and modern Generative AI libraries (e.g., LangChain, LlamaIndex, Azure AI Foundry). Hands‐on experience with PyTorch, leading LLM libraries such as Hugging Face, LangChain, LangGraph, and LlamaIndex. Skilled in hypothesis formulation, defining evaluation metrics, conducting literature reviews and building reproducible prototypes ...

several of the following: Commercial experience developing AI or Machine Learning solutions Strong Python development skills Experience with Machine Learning frameworks such as TensorFlow, PyTorch or Scikit-learn Experience with Generative AI technologies including LLMs, LangChain or LlamaIndex Hands-on experience building RAG solutions and working with vector databases Experience ...

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 ...

Requirements Vetting: Active SC Clearance and British Citizenship are mandatory for this consultancy role. Core Tech: Solid professional experience with Python and frameworks like PyTorch, TensorFlow, or Scikit-Learn. GenAI: Hands-on experience developing LLM-based applications, RAG pipelines, and prompt engineering. Infrastructure: Experience deploying applications in cloud environments ...

agents. Qualifications 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 ...

/fairness and emerging regulations. Machine Learning Mastery – profound understanding of ML principles, algorithms, frameworks, and training pipelines. Technical Proficiency – programming in Python, TensorFlow, PyTorch or similar; modern LLM/agent frameworks; cloud AI platforms (Amazon Bedrock, Azure OpenAI/AI Foundry, Google Vertex AI); vector databases; containerization and orchestration ...

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 ...

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 ...

Looking For in a Machine Learning Engineer Strong experience developing and deploying Machine Learning models using Python Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn or XGBoost Knowledge of AWS cloud services including SageMaker, Lambda and S3 Experience with MLOps tools such as MLflow, Weights & Biases or Data Version ...

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). Platform & Cloud Engineering: Architecture and delivery of scalable cloud data and AI platforms ...

with experience in data manipulation, model development, and analytical workflows. Hands‐on experience with machine learning frameworks such as scikit‐learn, TensorFlow, PyTorch, or similar. Strong experience with cloud platforms (AWS, Azure, or GCP) and building cloud‐native systems. Experience with modern data platforms such as Snowflake, BigQuery, or Databricks. ...

with a track record of building and maintaining production‐quality systems. Expert‐level Python experience with numerical, statistical, or ML libraries (e.g. NumPy, pandas, PyTorch, JAX, TensorFlow). Deep understanding of statistical modelling, optimisation, or machine‐learning techniques. Experience building and operating backend systems and data pipelines in a cloud ...

Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc. Experience monitoring, maintaining, enhancing existing models over an extended time period Extensive experience with PyTorch and related data science Python libraries (e.g. pandas) Experience of containerising applications or models for deployment (Docker) Experience with one of the major public cloud ...

methods. Familiarity with rigorous experimentation: online/offline evaluation, data leakage prevention, and model governance. Tech Stack Python, SQL, pandas/numpy/scipy, PyTorch/sklearn Airflow/dbt, Kafka (or equivalents), Postgres/BigQuery Docker, Kubernetes (optional), CI/CD (GitHub Actions) Observability: Prometheus/Grafana, OpenTelemetry ...

shipping models to production and supporting them post-deployment.* Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn).* Solid understanding of probability and statistical modeling to support robust model development and interpretation.* Experience with cloud platforms (especially Azure ...

Learning and ideally Azure AI Foundry Good understanding of MLOps and LLMOps Excellent Python engineering skills with experience using frameworks such as LangChain, LangGraph, PyTorch and Pydantic Experience with RAG, vector search, embeddings, prompt engineering and enterprise retrieval architectures Experience building AI solutions within document-heavy environments such as legal ...

LLMs, NLP, or computer vision projects Knowledge of AWS, Azure, or GCP Familiarity with Docker, APIs, or Git workflowsFamiliarity with frameworks such as PyTorch, TensorFlow, or Scikit-learn Internship, university project, or personal portfolio related to AI/ML What’s on Offer Competitive graduate salary package Company stock options ...

containerised application experience Infrastructure as code experience, ideally Terraform or CloudFormation Experience with SQL, PySpark, Databricks or AWS Glue Exposure to Scikit‐learn, PyTorch, LightGBM or similar libraries An understanding of LLMs, multimodal models or generative AI applications Deep commercial LLM experience is not essential. The hiring manager is more ...

Azure) and modern MLOps tooling (e.g. MLflow, Weights & Biases, DVC, Airflow) Strong experience with Python and associated scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit-learn, etc. Strong software engineering practices including version control, code review, software testing methodologies, and continuous integration; experience with ...

system integrations — with a strong focus on GenAI, NLP, computer vision, and multimodal AI applications. Leverage deep knowledge of AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and enterprise architecture best practices (e.g., microservices, APIs, cloud-native design). AI & Data Platform Architecture/Deployment: Architect robust, secure ...

technologies including Generative AI and Large Language Models. Required Experience: Commercial AI or Machine Learning experience. Strong Python and SQL skills. Experience with TensorFlow, PyTorch or similar frameworks. Understanding of machine learning and model deployment. Experience with Azure, AWS or GCP. Knowledge of data pipelines, ETL and MLOps. Strong analytical ...

/or Azure including managed ML services, scalable inference infrastructure, and vector stores. Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex, or equivalents). Enterprise AI Architecture: Designing AI systems for regulated production environments: data security, model explainability, audit ...

EMNLP, NAACL, ICLR) with specific focus on agent systems, tool use, or multi‐agent coordination. Familiarity with one or more deep learning frameworks (e.g. pytorch, jax, tensorflow, ...) Experience in ML Research beyond completing a PhD (e.g. supervision, industry experience, leading academic initiatives, ...). Excellent communication skills to report ...