plus. Hands-on experience with Google Cloud AI tools, such as Vision AI, Speech-to-Text, or AutoML. Proficiency in ML frameworks like TensorFlow, PyTorch, or Scikit-learn. Expertise in container orchestration for AI workloads using Google Kubernetes Engine (GKE) or Kubernetes. Background in software engineering, data science, or AI More ❯
or commercial experience is a plus Strong understanding of mathematical background, focusing on statistics and linear algebra Highly proficient in Python (Pandas, Scikit-Learn, PyTorch, PySpark) and SQL Experience with Snowflake (function & procedure) and Snowpark is a plus Experience with unit and integration tests Strong understanding of machine learning algorithms More ❯
techniques within VectorDBs. Expert programming skills in languages such as Python, Java, or C++, and working knowledge of deep learning frameworks such as TensorFlow, PyTorch, or Hugging Face. Understanding of generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models (e.g., GPT, BERT for text). More ❯
experience, or an MS with significant industry or research experience in the field. Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned More ❯
re comfortable developing or learning to develop custom metrics, identify biases, and quantify data quality. Strong Python skills for Data & Machine Learning, familiarity with PyTorch and TensorFlow. Experience with distributed computing and big data - scaling ML pipelines for large datasets. Familiarity with cloud-based deployment (such AWS, GCP, Azure, or More ❯
degree in computer science, or related technical, math, or scientific field. Proven knowledge of deep learning and experience using Python and frameworks such as Pytorch, TensorFlow. Proven knowledge of Generative AI and hands-on experience of building applications with large foundation models. Experiences related to AWS services such as SageMaker More ❯
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 More ❯
Programming (NLP), Large Language Model (LLM), or Large Computer Vision Models. Use SQL to query and analyze the data. Use Python, Jupyter notebook, and Pytorch to train/test/deploy ML models. Use machine learning and analytical techniques to create scalable solutions for business problems. Research and implement novel More ❯
Familiarity with containerization (Docker) Understanding of RESTful APIs and microservices architecture. Experience with AI/ML-related tools and libraries (e.g., Hugging Face, LangChain, PyTorch) preferred A strong intuition for what makes products a joy to use. Empathy for how different users will need different things out of a product More ❯
high availability. Very strong technical background leading application development - with experience in some or all of the following technologies: Python, Java, Spring Boot, TensorFlow, PyTorch, Apache Spark, Kafka, Jenkins, Git/Bitbucket, Terraform, Docker, ECS/EKS, IntelliJ, JIRA, Confluence, React/Typescript, Selenium, Redux, Junit, Cucumber/Gherkin. About More ❯
An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example More ❯
communicating across technical and non-technical audiences. Experience in using Python and hands-on experience building models with deep learning frameworks like TensorFlow, Keras, PyTorch, MXNet. Fluency in written and spoken English. Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our More ❯
AI/ML software. Strong expertise in machine learning and neural network algorithms. Proficient in programming languages like Python, and libraries such as TensorFlow, PyTorch, NumPy and LangChain Experience with server deployment, cloud computing environments, and API development. Good understanding of software engineering best practices. Excellent problem-solving abilities and More ❯
AI/ML software. Strong expertise in machine learning and neural network algorithms. Proficient in programming languages like Python, and libraries such as TensorFlow, PyTorch, NumPy and LangChain Experience with server deployment, cloud computing environments, and API development. Good understanding of software engineering best practices. Excellent problem-solving abilities and More ❯
Birmingham, England, United Kingdom Hybrid / WFH Options
Talent
the Civil Service. Key Skills and Experience • Strong programming skills in Python, R, or SQL. • Experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). • Proficiency in data manipulation and analysis (e.g., Pandas, NumPy, Spark). • Knowledge of data visualization tools (e.g., Power BI, Tableau, Matplotlib). • Understanding of More ❯
of experience in machine learning engineering, with a strong focus on productionizing ML models and MLOps. Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines More ❯
with a self-starter mindset. Proven track record with NLP applications and transformer-based models like BERT or GPT. Advanced proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. More ❯
with a self-starter mindset. Proven track record with NLP applications and transformer-based models like BERT or GPT. Advanced proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. More ❯
development. Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch). Skills & Competencies: Must Have: Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets, data wrangling, and data preprocessing. More ❯
Education & Experience – Strong academic background in Data Science, Machine Learning, AI, or a related field. Technical Skills – Proficiency in Python, SQL, ML frameworks (TensorFlow, PyTorch, scikit-learn), and at least one OOP language (Rust, C++, C#). AI & NLP Expertise – Hands-on experience with LLMs, NLP, and AI-driven data More ❯
building language model applications. Proficiency in Python and SQL, with strong skills in data manipulation and analysis. Expertise in AI frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. Ability to effectively communicate complex AI concepts, especially to non-technical stakeholders. Preferred Qualifications Experience with graph databases and knowledge graphs. More ❯
Education & Experience – Strong academic background in Data Science, Machine Learning, AI, or a related field. Technical Skills – Proficiency in Python, SQL, ML frameworks (TensorFlow, PyTorch, scikit-learn), and at least one OOP language (Rust, C++, C#). AI & NLP Expertise – Hands-on experience with LLMs, NLP, and AI-driven data More ❯
in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world environments. A passion for AI innovation and a desire More ❯
in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world environments. A passion for AI innovation and a desire More ❯
and other traditional machine learning models, translating conceptual ideas into actual solutions. Fluent in some of these machine learning frameworks such as SKLearn, XGBoost, PyTorch, or Tensorflow. Proficient in Python and able to transform abstract machine learning concepts into robust, efficient, and scalable solutions. Strong Computer Science fundamentals and object More ❯
