12 of 12 TensorFlow Jobs in Central London

Required Experience & Skills AI/Technical Strong experience building and deploying machine learning and AI solutions in production. Hands‐on experience with: Python (PyTorch, TensorFlow, scikit‐learn) Data engineering tools (SQL, Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure ...

maximum scalability and cost-efficiency. Insurance Domain experience is added advantage. Your Technical Toolkit AI/ML: Expert proficiency in Python , PyTorch/TensorFlow, and GenAI frameworks like LangChain, LlamaIndex, or CrewAI . Data Architecture: Hands-on experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and SQL/NoSQL ...

users) machine learning solutions for personalizing recommendations. Hands-on experience building training frameworks and/or serving large-scale models using tools such as Tensorflow or PyTorch is a plus. You stay up-to-date with the state-of-the-art open source infrastructure solutions applicable to designing ...

working as a data scientist (or similar role). Expert proficiency of Python and experience using SQL, alongside using common ML/AI packages (TensorFlow, PyTorch, XGBoost, etc.). Familiarity with statistical (e.g. GLM, GAMs) and deep learning techniques (e.g. neural networks, transformers, diffusion models, etc.). Comfortable designing ...

Learning Engineer or equivalent role Hands-on experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version ...

Engineer or Data Scientist with production ML exposure Strong understanding of ML algorithms, model development, and deployment Experience with Python and ML frameworks (e.g. TensorFlow, PyTorch, scikit-learn) Familiarity with data pipelines and cloud or on-prem environments Understanding of MLOps principles Ability to work in secure or regulated ...

ICML, or ICLR. Hands-on experience applying deep learning models to complex, ideally temporal or financial, datasets. Expertise in Python and modern ML frameworks (TensorFlow, PyTorch, JAX, etc.). Prior experience in a commercial or industry setting post-PhD, ideally in a similarly data-driven domain. Why Apply Compensation ...

distributed computing tools such as Azure Synapse and Snowflake. Familiarity with data transformation tools (Databricks, Azure Data Factory) and ML platforms (Azure ML, TensorFlow). Experience with data visualisation tools such as Power BI. Strong commercial acumen with the ability to translate business problems into scalable analytical solutions. Excellent ...

system design: orchestration, memory, deployment, monitoring – Exposure to tools such as LangGraph, ReAct, CoT — or building similar systems from scratch – Familiar with PyTorch/TensorFlow, RAG architectures, vector databases, modern cloud infra – Experience in startup or consulting environments is a plus – Comfortable working autonomously in fast-moving, sometimes ambiguous ...

Strong expertise in deep learning, neural networks, and generative models (diffusion, GANs, transformers). Hands-on experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow). Advanced programming skills in Python. Solid understanding of model training, evaluation, and scaling. Strong problem-solving, analytical, and communication skills. Ability to work ...

personalisation engines Owning experimentation, A/B testing, and performance metrics Mentoring engineers and setting technical direction across teams Tech Stack Python PyTorch/TensorFlow MLOps (CI/CD, model monitoring, retraining pipelines) Real-time/streaming systems Cloud-based ML infrastructure What You’ll Bring Strong experience building ...

engineering roles Proven track record of deploying ML models into production environments Strong Python skills and deep experience with frameworks such as PyTorch or TensorFlow Experience working with complex, high-dimensional datasets (biological data strongly preferred) Solid understanding of modern ML approaches (deep learning, representation learning, etc.) Experience leading ...