23 of 23 Amazon SageMaker Jobs in the UK

/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 ────────────────────────── UK Remote Competitive Salary and benefits Not eligible for visa sponsorship ...

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 ...

language models. Strong grounding in ML concepts — supervised, unsupervised, and reinforcement learning. Familiarity with cloud AI/ML services (e.g. Azure Cognitive Services, AWS SageMaker, and/or GCP Vertex AI). Experience deploying and monitoring AI workloads in CI/CD environments (e.g. Jenkins, Docker, Kubernetes, and/ ...

Docker, Kubernetes desirable) Model packaging and REST APIs Cloud & Tools (Any combination) AWS/Azure/GCP analytics and ML services MLflow, Azure ML, SageMaker, Vertex AI Version control (Git) Domain & Soft Skills Strong analytical and problem solving skills Ability to explain complex data science concepts in simple business ...

systems (e.g. forecasting, optimisation, CV) • Solid ML Ops and data engineering experience • Proficiency in Python & SQL (Airflow, PyTorch, Spark a plus) • Experience with AWS (SageMaker, Lambda, etc.) • Interest in backend development (Django or Go) • High level STEM Degree from a leading university. Nice to have: • Experience with data lakes ...

some of the following tools; Apache Spark, Databricks, Pandas for data processing and feature engineering SQL, MongoDB for structured and unstructured data management SageMaker, Azure ML for model deployment, tracking, and monitoring. Terraform, Ansible, GitLab CI/CD, Jenkins for infrastructure automation and DevSecOps ...

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 ...

Generative AI models, LLMs, and agentic frameworks. •Collaborate with stakeholders to define problem statements and solution approaches. •Deploy models using MLOps tools such as SageMaker, Snowflake, and CI/CD pipelines. •Integrate vector databases and Retrieval-Augmented Generation (RAG) pipelines. •Ensure high-quality delivery and adherence to best practices ...

Degree in a relevant technical field Tech Environment Python, Java Cloud platforms AWS Containerisation and infrastructure-as-code tools Relational and non-relational databases Amazon Bedrock, Sagemaker, Vertex AI Datadog, Databricks, MLFlow, Grafana, OTel Claude, Gemini, ChatGPT, open-weight LLM models Terraform, Docker, K8s RMDBs, Graph DBs, Vector ...

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 ...

technical audiences. Understanding of Information Governance, ethics, and AI explainability. Excellent problem-solving, organisational, and communication skills. Desirable Cloud computing (Azure ML, AWS SageMaker), APIs, Git Data engineering or DevOps awareness Familiarity with interoperability standards (FHIR, HL7, OpenEHR). Awareness of national data strategies (Data Saves Lives, PHM Framework ...

Sourcegraph & Meta. What We Are Looking For MLOps & DevOps Tooling: Proven track record of working with systems such as ArgoCD, Kargo, Jenkins, Vertex AI, SageMaker, and similar platforms. ML Infrastructure Experience: Proven track record of working on ML research and production infrastructure. Infrastructure as Code: Knowledge of IaC tools ...

reliability of ML systems What We're Looking For Proven experience with ML in production environments Strong skills in Python, SQL, and AWS (SageMaker, Lambda, etc.) Familiarity with MLOps and real-time data applications A pragmatic mindset and strong problem-solving ability Background in adtech or fraud detection ...

/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 ...

data platform architecture • Define governance, data modelling, and information architecture standards • Lead selection and implementation of AI technologies (AWS stack including Bedrock, SageMaker, Amazon Q) • Partner with senior stakeholders across data, engineering, and business functions • Guide a small team and work with external delivery partners We’re looking ...

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 ...

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 ...

Core Capabilities Enterprise AI & Architecture Agentic AI, RAG, enterprise ML in regulated FS Azure AI Studio, Azure AI Search, Fabric, Document Intelligence AWS Bedrock & SageMaker pattern experience FastAPI, Kubernetes, Docker, CI/CD (ADO/GitHub/GitLab) Secure landing zones, policy-driven cloud governance, FinOps Vector, Data & Integration … Azure-native sources to enable analytics and GenAI readiness. Cross-Cloud FS AI Enablement Led design patterns portable across Azure + AWS Bedrock/SageMaker for regulated workloads. Developed controls for data boundary enforcement & model validation pipelines. GenAI & Cloud Solutions Architect Accelerated AI value from PoC pilot scaled capability ...

machine learning solutions Strong skills in Python, SQL, and ML libraries (e.g. scikit-learn, XGBoost, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda, Redshift) Ability to clearly communicate insights to non-technical stakeholders Strong analytical thinking, collaboration skills, and a results-driven mindset Role responsibilities: Build … tune, and maintain predictive and ML models using AWS SageMaker Analyse large datasets and perform feature engineering to improve model performance Run experiments, test hypotheses, and optimise models for accuracy and value Monitor model performance and manage retraining over time Collaborate with Data Engineers, BI Developers, and Analysts ...

model performance, data drift, and system health to ensure reliability and availability. Support ML platforms and infrastructure on-premise or in the cloud (AWS, SageMaker), ensuring scalability and security. Collaborate with data scientists to productionise models and embed ML Ops best practices across the organisation. Ensure governance, compliance, documentation … model maintenance. Hands-on experience with Python (and ML frameworks), containerisation (Docker/Kubernetes), CI/CD pipelines, and cloud ML services (AWS SageMaker preferred). Experience with infrastructure-as-code, production-grade Linux environments, and API services (Flask/Gunicorn). Proficiency in building automated, reliable ML pipelines ...

complex business requirements into robust data solutions. The Role: You will tackle complex data challenges in a fast-paced, exciting environment, leveraging cutting-edge Amazon Web Services (AWS) technologies such as Lambda, SageMaker and Bedrock to develop innovative and scalable data solutions. RealityMine has an out of hours ...

Title: Lead Machine Learning Engineer (SageMaker, MLOps, Explainability) Job Description We are seeking an experienced Lead Machine Learning Engineer to design, build, and productionise machine learning models for our innovative matching platform. You will work across the entire ML lifecycle, from feature engineering to deployment automation, ensuring the optimisation … auditability. Conduct validation of models using golden datasets and baseline tests. Essential Skills Strong experience delivering production-grade ML systems. Proficiency with AWS SageMaker, including training jobs and Model Registry. Excellent skills with ML models like PyTorch, TensorFlow, or XGBoost. Hands-on experience with model explainability tools such ...

governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift … closely with Engineering/Data teams in production-first environments Comfortable operating within regulated industries Desirable AWS experience (S3, Athena/Glue, IAM, Lambda) SageMaker or equivalent ML platform experience Financial services domain knowledge (risk, fraud, affordability, payments) Experience with model explainability and governance documentation Package & Benefits Hybrid working ...