6 of 6 Permanent Amazon SageMaker Jobs in the City of London

Models (SLMs). Infrastructure & Operations Containers: Advanced knowledge of Docker, Podman, and container orchestration. Cloud Providers: Practical experience managing AI workloads on AWS (EC2, SageMaker), Azure (Azure ML), and GCP (Vertex AI). CI/CD Tools: Experience with GitLab CI, GitHub Actions, Jenkins, or specialized MLOps platforms (e.g. ...

Technical Knowledge: Strong understanding of modern AI/ML technologies and cloud-based solutions. Tools & Platforms: Familiarity with Snowflake, Databricks, Azure AI Foundry , AWS SageMaker, and other leading platforms. Data Science Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural ...

Python, applied AI, Generative AI, prompt engineering, retrieval-augmented generation (RAG), vector stores, and agentic architectures. Hands-on familiarity with platforms like Databricks, Snowflake, SageMaker, or Vertex. Agent lifecycle management, observability, and governance frameworks.Agentforce & Salesforce Platform Deep architectural understanding of how Data and AI are activated within Salesforce — including ...

experience - S3, Lambda, EventBridge, SNS/SQS Comfortable working across TOGAF, ArchiMate, C4, UML and BPMN Hands-on with MLOps tooling and platforms - SageMaker, OpenVINO or equivalent Experience architecting data for large volumes of imagery - tiering, retention, lineage, provenance Familiar with the governance side of AI - DPIAs, model documentation ...

vector operations using numpy, scipy; experience with distributed computation using multithreading, multi GPUs, Dask, Ray, Polars Familiarity with AWS Cloud services such as EMR, Sagemaker Overall knowledge of the Software Development Life Cycle Solid understanding of agile methodologies including CI/CD, application resiliency, and security Preferred Qualifications, Capabilities ...

integration Lead architectural decisions across the ML stack, ensuring scalability, reliability, and alignment with business goals Drive the ongoing migration from MLflow to AWS SageMaker, maintaining continuity and minimising disruption Define and enforce MLOps best practices across model training, serving, monitoring, and deployment MLOps & Engineering Design and maintain scalable … data engineering, and large-scale data processing Experience with containerisation (Docker) and scalable data systems (e.g. Spark, Kafka) Nice-to-Have Experience with AWS SageMaker or similar managed ML platforms Background in safety-critical or regulated industries (aerospace, aviation, or similar) Familiarity with Kafka or event-driven architectures ...