18 of 18 Permanent Keras Jobs

Engineer to join our growing teams in Erskine or Newcastle. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using More ❯

and PyTorch. Programming: Solid experience in Python and SQL. Experience with R is a nice-to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on cloud like Azure More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

s3) Experience with software development life cycle (sdlc) and agile/iterative methodologies Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and More ❯

foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression, classification, clustering, time-series forecasting). Practical experience with Keras or PyTorch is required. Full-Stack Deployment: Demonstrable experience taking models to production, including building and deploying APIs with FastAPI and using Vertex AI for ML workflows. Visualization & Communication: Ability More ❯

skills, a quick learning ability, and enthusiasm for tackling complex challenges. You are proficient in Python, with experience using PySpark and ML libraries such as scikit-learn, TensorFlow, or Keras . You are familiar with big data technologies (e.g., Hadoop, Spark), cloud platforms (AWS, GCP), and can effectively communicate technical concepts to non-technical stakeholders. Accommodation requests If you need More ❯

enabling efficient data transformation for downstream machine learning modeling and analysis. 3. Utilize knowledge of advanced data science techniques and tools, including Python, Apache Spark, SparkML, scikit-learn, TensorFlow, Keras, XGBoost, and Tidyverse, to build various ML-driven applications such as recommendation models, timeseries forecasting, attribution models that address business needs and deliver impactful solutions. 4. Utilize knowledge of AWS … large-scale data processing, enabling seamless preparation for downstream modeling and analysis. 10. Utilize Python, machine learning, supervised and unsupervised algorithms, and statistical modeling with SparkML, scikit-learn, TensorFlow, Keras, XGBoost, and Tidyverse to build large-scale machine learning models for clustering, segmentation, cancer detection, etc. 11. Utilize AWS SageMaker and Google Cloud Platform for scalable batch inferencing and modeling More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

team members in identifying and addressing data science opportunities. Required Skills & Experience Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using … problem-solving skills and ability to work independently or in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using More ❯

LLM features in a regulated environment with sensitive data. You’ll work with engineering to move models from idea to production. Tech Stack: Python, scikit-learn, PyTorch, TensorFlow/Keras, LangChain, Pydantic, Spark, Kafka, Databricks, MLflow, Azure, Docker, Kubernetes. Salary: Up to £100k Working Environment: Fully Remote in UK What you’ll do Research, prototype, and deploy LLM use cases … Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you’ll bring Hands-on Python for data science and ML Practical experience with LLM frameworks (e.g. LangChain) Familiar with Spark More ❯