1 to 25 of 186 PyTorch Jobs in Central London

Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker More ❯

Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI More ❯

for sensitive environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3 More ❯

RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in technology deployment, including More ❯

Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models. Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components. Responsibilities also include More ❯

production. What We’re Looking For 3+ years of experience in Data Science. Strong proficiency in Python and experience with ML libraries/frameworks (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms More ❯

involvement in LLM projects including training, fine-tuning, or production deployment Strong track record in scalable AI system development Technical Expertise Expert-level Python programming with deep knowledge of PyTorch, TensorFlow, or JAX Proven experience with distributed computing environments and modern MLOps workflows Practical knowledge of LLM toolkits (Hugging Face ecosystem, training frameworks) Strong problem-solving abilities for complex model More ❯

contribute to the broader ML/NLP community Technical Skills: Degree in Computer Science, Engineering, Bioinformatics, or related field (BSc, MSc, PhD) Experience with ML/NLP frameworks (e.g., PyTorch, TensorFlow, HuggingFace, Scikit-learn) Strong Python skills and familiarity with additional languages (e.g., Java, C++) Understanding of biomedical ontologies, knowledge graphs, or causal inference is a plus Familiarity with cloud More ❯

For 1–3 years of experience in AI engineering, data engineering, or software development Demonstrable experience delivering AI, LLM, or data-driven applications Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn) and/or TypeScript/JavaScript Familiarity with LLM frameworks (LangChain, LlamaIndex) and vector databases (Pinecone, Weaviate) Understanding of cloud platforms (AWS, Azure, or GCP) and modern More ❯

across a variety of languages, frameworks, and tooling, such as Python, R, Kubernetes, Kubeflow, JavaScript/JVM, Kafka Background in Application development, deployment, and monitoring on Kubernetes Proficient in PyTorch or similar frameworks (e.g Tensorflow) – demonstrates strong understanding of neural networks and LLMs; familiar with frameworks widely used in Hugging Face models, LLaMA, and similar architectures Expertise in fine-tuning More ❯

Platform (GCP) services, such as Vertex AI, BigQuery, Cloud Functions, and Cloud Storage. Strong programming skills in Python (preferred), Java, or similar languages, including experience with ML frameworks (TensorFlow, PyTorch, etc.). Familiarity with regulatory environments in banking/finance and understanding of regulatory change management processes. Knowledge of automation, workflow orchestration, and version control tools (e.g., Airflow, Git). More ❯

of models. Pilot emerging technologies. Experience & Skills 2–4 years in production-level AI/ML delivery. Legal/professional services experience is a plus. AI/ML frameworks: PyTorch, TensorFlow, LangChain. Cloud: Azure (preferred), AWS, GCP. MLOps: CI/CD, model lifecycle, monitoring. Generative AI: LLMs, RAG, chat agents. Data engineering alignment: ETL, governance. Strong coding, communication, and collaboration More ❯

of models. * Pilot emerging technologies. Experience & Skills * 2-4 years in production-level AI/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration More ❯

and deep learning architectures Strong understanding of two-tower neural networks, embedding techniques, and ranking models Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work cross-functionally with marketing, CRM, and More ❯

and unsupervised learning, NLP, neural networks, Bayesian statistics, time-series forecasting, collaborative filtering, and deep learning Proficiency in Python and ML libraries (e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Experience with cloud platforms (GCP, AWS, Azure) and tools like Dataiku and Databricks Experience with ML Ops, including deployment, monitoring, and retraining pipelines Ability to work cross-functionally with marketing More ❯

as you grow What We’re Looking For: 🎓 A degree in Artificial Intelligence, Computer Science, Mathematics, or a related field 🐍 Proficiency in Python and experience with frameworks such as PyTorch , NumPy , or Scikit-learn 🧩 Understanding of machine learning algorithms, optimisation, and model evaluation 📈 Strong mathematical reasoning and analytical problem-solving skills 🗣 Excellent communication skills and a genuine passion for learning More ❯

engineering skills, especially in Python. Experience building robust systems for ML applications Proven track record deploying machine learning models in production (using frameworks such as Scikit-learn, TensorFlow, or PyTorch) Practical experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and a good understanding of architecture, security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering More ❯

data processing and training models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

and training models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms More ❯

and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms More ❯

reviews, architectural decision-making, and technical debt management Ability to establish coding standards and best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance More ❯

in AI/ML, with at least 1 year focused on Generative AI and NLP. Strong programming skills in Python; proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen More ❯