9 of 9 Permanent Amazon SageMaker Jobs in Central London

record building and maintaining CI/CD pipelines (GitHub Actions, CircleCI, Jenkins, GitLab CI) • Experience with MLOps and supporting ML model deployment workflows (AWS Sagemaker, Lambda, containerized deployments) • Proficiency in scripting and automation (Python, Bash, Go) • Strong experience with monitoring and observability tools (CloudWatch, Prometheus, Grafana, Datadog, Sentry ...

adapters), RAG , and evaluation (golden sets, BLEU/BERTScore/semantic similarity). Cloud : Azure (incl. Azure AI, OpenAI, Cognitive Services ), AWS (Bedrock/SageMaker), GCP (Vertex AI); containerization (Docker/Kubernetes) and service meshes. Data & Integration : SQL, NoSQL, data warehousing concepts (fact/dimension, SCD), ETL/ ...

models using Python, PyTorch and modern ML frameworks Work with large datasets to train, evaluate and optimise models Implement and scale solutions on AWS (SageMaker, Bedrock, Lambda, etc.) Contribute to generative AI initiatives using Hugging Face and open-source models Collaborate with data engineers, architects and stakeholders to deliver ...

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 ...

endpoints Key details: • Salary: £80,000–£110,000 + discretionary bonus • Working: Hybrid, 3 days per week in London • Stack: Python, MLflow, AWS (SageMaker), CDK, Docker • Visa: Cannot sponsor Interested? Please apply below. ...

pace of a scale-up. Requirements 2+ years MLOps experience Strong Python, ML engineering and deployment foundations. Hands-on experience with AWS and SageMaker (essential). MLOps best practice: MLflow, CI/CD for ML, monitoring, reproducibility. Cloud and DevOps knowledge: AWS, Docker, IaC (CDK preferred). Experience deploying ...

data processing, and feature engineering experience. Strong statistical and analytical capabilities. Experience in start-up or scale-up environments. Desirable MLOps tools (MLflow, Kubeflow, Sagemaker, etc.) Experience in PropTech or property data. Cloud experience (AWS/GCP/Azure). Docker/Kubernetes, CI/CD, and event-driven ...

Frameworks: Hands-on experience with some of the following LangChain/LangGraph, LlamaIndex, PyTorch , and FastAPI . Modern Infrastructure: Experience with AWS Bedrock/SageMaker and using Docker/Kubernetes to scale agent instances. RAG Expertise: Proven experience building RAG systems; experience with Graph-RAG (e.g., Neo4j ...

intersection of modelling, engineering, and intelligent automation. 🌐 What You’ll Do 🧠 Model Development & AI Systems Design Design and train predictive models using AWS SageMaker Develop LLM-powered systems via AWS Bedrock (including Claude integration) Build RAG pipelines combining structured and unstructured data Develop evaluation frameworks for accuracy, bias … powered decision layers into enterprise processes Design human-in-the-loop feedback systems to improve performance ☁️ AWS ML Infrastructure Deploy and manage models using SageMaker (training, endpoints, pipelines) Leverage Bedrock for foundation model access Implement serverless inference with Lambda & API Gateway Utilise S3, Glue, Athena for data processing Implement ...