12 of 12 Permanent Amazon SageMaker Jobs in London

experience developing and deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with AWS ML services (SageMaker, Lambda, S3) in production environments Strong experiment design skills: hypothesis formulation, A/B testing methodology, and statistical evaluation Proven track record transitioning models ...

across one or more CSPs or third-party vendor technologies, including Google PaLM and Vertex AI, Azure OpenAI Services and AzureML, AWS Bedrock and Sagemaker, Dataiku, Databricks, Snowflake SnowPark and Snowflake Cortex, Data Robot. Experience in architecting scalable, performant & cost optimized AI/ML solutions leveraging; serverless technologies, container ...

some of these, but if not we’ll help you with them: Good knowledge of AWS cloud services (e.g. Lambda, ECS, Bedrock, S3, Sagemaker) ML Engineering and MLOps knowledge (e.g. Docker, ML flow) Experience working with government or other carefully regulated industries Track record of applying LLMs to robustly ...

NumPy, Pandas, SciPy, Scikit‐learn, TensorFlow, PyTorch, Transformers, Langchain, Streamlit, or Gradio, among others. Experience with cloud‐based tools for production use (e.g., AWS SageMaker, AWS Bedrock, Vertex AI, Azure Machine Learning, Azure OpenAI). Knowledge of databases and data technologies, such as Snowflake, BigQuery, and relational databases like ...

Python and SQL skills Understanding of data platforms (e.g. Snowflake/CDP) and ingestion pipelines Exposure to AI/ML enablement frameworks (.e.g AWS SageMaker) and supporting infrastructure for model training, deployment Familiarity with modern development and deployment frameworks Experience with dbt/ELT patterns (beneficial, not core focus ...

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 ...

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 ...

systems (e.g. RAG, agentic workflows, NLP). Strong product and systems thinking with a practical, impact-led mindset. Nice to Have AWS experience (e.g. SageMaker, Bedrock, Lambda, S3, Redshift). Familiarity with MLOps practices. Background in adtech, media, fraud, or adversarial systems. Why Join Work on technically challenging, high ...

/ML engineering, shipping models in production • 3+ years in a Staff-level or senior technical leadership role • Hands-on AWS (Bedrock, SageMaker) and/or GCP Vertex AI • Strong Python; Java or Rust a bonus • Experience with LLMs, evaluation frameworks, and agentic AI patterns • Docker, K8s, Terraform …/CD • A collaborative leadership style — mentoring engineers, shaping best practices ────────────────────────── Core stack: Python · Rust · Java · Amazon Bedrock · SageMaker · Vertex AI · LangGraph · Pydantic AI · LangFuse · MLFlow · Databricks · Datadog · Claude Code · Docker · K8s · Terraform · Vector DBs · Graph DBs ────────────────────────── London hybrid (2 days/week) Competitive Salary and benefits ...

rich metadata, automated quality profiling, and governance guardrails. Consumers: Reduce friction from discovery to access — self-service provisioning, entitlement workflows, one-click integration with SageMaker, Databricks, and EMR. Network effects: Analyze usage trends to improve data quality, discovery and relevancy across persona groups Collaborate with Engineering, Design & Data Science ...

platforms Establish best practices around: Model governance; Monitoring and retraining; Environment management Integrate with cloud and data platforms such as Databricks, and potentially AWS SageMaker Essential Experience Strong MLOps background, not just theoretical knowledge Extensive hands-on MLflow experience (non-negotiable) Demonstrable experience productionising ML models for at least … MLOps-led) Experience designing and supporting ML platforms in production environments Technical Skills (Required/Highly Desirable) MLflow Databricks Cloud platforms (AWS preferred; SageMaker experience a plus) CI/CD for ML (e.g. GitHub Actions, GitLab CI, Azure DevOps, etc.) Containerisation and orchestration (Docker, Kubernetes) Infrastructure as Code (Terraform ...

dashboards, alerting, and MS Teams integrations—and reduce incidents via telemetry-driven automation and blameless postmortems. Champion AI‐enabled operations using AWS Bedrock/SageMaker and Model Context Protocol (MCP) for log analysis, anomaly detection, incident triage, and workflow orchestration; establish adoption guardrails. Define and enforce cloud guardrails … operations—incident triage, runbooks, and change management—with clear guardrails, auditability, and human-in-the-loop controls. Supporting AI/ML workloads at scale: SageMaker endpoints, GPU node groups, autoscaling, and Kubernetes-based model serving. Policy-as-code (OPA) and compliance implementation across CIS, NIST, ISO 27001, with automated ...