1 to 25 of 108 TensorFlow Jobs in Central London

organisation. 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 More ❯

organisation. 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 More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

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 ❯

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 ❯

on experience in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience More ❯

on experience in data science and AI engineering. Proven track record of delivering AI software and deploying solutions in production. Strong proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Keras, Numpy, Pandas, Langchain, Pinecone, and more. Experience with cloud platforms and compute systems for model training and deployment. Excellent communication and strategic thinking skills. Preferred Experience More ❯

ML 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 More ❯

ML 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 More ❯

in 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/ More ❯

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 development workflows More ❯

We’re Looking For: Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role. Strong proficiency in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes). Solid understanding of CI/CD systems (e.g., GitHub Actions, GitLab More ❯

We’re Looking For: Proven experience in a senior-level DevOps, MLOps, or related infrastructure-focused engineering role. Strong proficiency in Python and familiarity with ML frameworks such as TensorFlow or PyTorch. Deep experience with cloud platforms (AWS, GCP, or Azure) and container orchestration tools (Docker, Kubernetes). Solid understanding of CI/CD systems (e.g., GitHub Actions, GitLab More ❯

Cloud 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 More ❯

Cloud 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 More ❯

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 data More ❯

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 data More ❯

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 (e.g. More ❯

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 (e.g. More ❯

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 (e.g. More ❯

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 (e.g. More ❯

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 methodologies – including data collection More ❯

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 methodologies – including data collection More ❯

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 ❯