10 of 10 Remote/Hybrid Amazon SageMaker Jobs in London

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, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure ...

science Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost Exposure to AWS services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation, and experimentation Familiarity with MLOps or experiment tracking tools (e.g. MLflow, Weights & Biases) Awareness of LLMs ...

/CD pipelines. You can set up APIs, write robust code, and debug pipelines. ● Experience with cloud-native ML workflows, including AWS Sagemaker, Lambda, ECS, and containerisation with Docker. ● Familiarity with frontend collaboration—how your model outputs can drive dynamic UIs, content personalization, or editor tools. ● Comfort in “hacking ...

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 Control) Experience developing LLM/GenAI applications, including prompt engineering ...

path to production - Integrate ML infrastructure with CI/CD systems and cloud platforms Required Skills & Qualifications: - Strong experience with ML platforms — MLflow, Kubeflow, SageMaker, or Vertex AI - Proficiency in Python and infrastructure tooling (Docker, Kubernetes, Terraform) - Experience building automated model training and deployment pipelines - Familiarity with feature stores ...

facing or product-centric ML systems. A product-focused mindset, balancing experimentation with real-world value. Useful additional experience includes: AWS or GCP environments (SageMaker, Bedrock, Lambda, S3, Redshift). MLOps practices (monitoring, deployment, optimisation). Working with streaming data, transformer models or recommendation systems. What They Offer ...

Machine learning (supervised & unsupervised) Generative AI & LLMs Embeddings, vector databases, RAG MLOps and model lifecycle management Experience with platforms such as: Azure ML, AWS SageMaker, Google Vertex AI Databricks, LangChain, Hugging Face Ability to develop architecture artefacts (HLDs, solution diagrams, delivery models) Exceptional writing, storytelling, and stakeholder engagement skills ...

frameworks (PyTorch, TensorFlow, or XGBoost) - Experience with end-to-end ML pipelines from data ingestion to model serving - Familiarity with ML platforms (SageMaker, Vertex AI, MLflow, or Kubeflow) - Solid understanding of supervised, unsupervised, and reinforcement learning fundamentals - Experience with feature stores, model versioning, and experiment tracking - Strong software engineering ...

core ML libraries (PyTorch, TensorFlow, scikit-learn) - Experience building and deploying LLM integrations or fine-tuned models - Familiarity with model serving infrastructure (e.g., SageMaker, Vertex AI, BentoML) - Solid understanding of vector databases, embeddings, and retrieval systems - Ability to communicate technical findings clearly to non-technical stakeholders Equal Opportunity Employer ...

translate business needs into technical solutions Required Skills & Experience: Active SC Clearance (mandatory) Strong proficiency in Python Hands-on experience with AWS , particularly SageMaker Experience with Streamlit for rapid prototyping and PoC development Solid understanding of anomaly detection techniques Experience building data workflows/pipelines Desirable: Experience working ...