5 of 5 Remote/Hybrid Amazon SageMaker Jobs in Central London

/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 ...

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 ...

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 ...