25 of 25 Permanent Amazon SageMaker Jobs in London

pipelines, and custom model fine-tuning. Working closely with architects and data teams, you will turn conceptual designs into robust, production-grade software using Amazon Bedrock and modern Python frameworks. Key Responsibilities · Agent Development: Build and deploy sophisticated AI agents using Python, LangChain, and LangGraph. Implement tool calling (function … calling), memory persistence, and error-handling logic for robust autonomous execution. · RAG Implementation: Develop high-performance retrieval pipelines. Optimise chunking strategies, embedding generation (using Amazon Titan or similar), and vector search retrieval in Amazon OpenSearch. · Model Fine-Tuning: Execute fine-tuning jobs on Amazon SageMaker, preparing ...

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 ...

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 ...

science Strong Python skills with experience in ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost Exposure to AWS services (e.g. S3, Lambda, SageMaker) Understanding of core ML concepts: model evaluation, validation, and experimentation Familiarity with MLOps or experiment tracking tools (e.g. MLflow, Weights & Biases) Awareness of LLMs ...

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 ...

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 ...

more cloud platforms or third-party technologies such as Google PaLM and Vertex AI, Azure OpenAI Services and Azure ML, AWS Bedrock and SageMaker, Dataiku, Databricks, Snowflake Snowpark and Snowflake Cortex, and DataRobot. Experience architecting scalable, high-performance, and cost-optimised AI and ML solutions using serverless technologies, containerised ...

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 ...

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 ...

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 ...

Machine Learning Architect/GenAI Architect Hands-on expertise with LLMs, RAG, LangChain, LangGraph, prompt engineering Strong cloud experience: Azure OpenAI, AWS Bedrock/SageMaker or GCP Vertex AI Experience with Kubernetes, Docker, microservices, API integration Strong knowledge of Python, MLOps, LLMOps, CI/CD, model monitoring Experience delivering ...

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 ...

Architect Strong hands-on expertise with LLMs, RAG architectures, LangChain, LangGraph, and prompt engineering Deep cloud knowledge including Azure OpenAI, AWS Bedrock/SageMaker, or GCP Vertex AI Experience with Kubernetes, Docker, microservices architectures, and API integrations Proficiency in Python, MLOps, LLMOps, CI/CD pipelines, and model monitoring ...

concepts clearly and effectively to a range of audiences. Experience with model governance, documentation, and deployment best practices. Experience with cloud environments (e.g., AWS Sagemaker). Experience with collaborative development tools (e.g., Git, JIRA) is a plus. Prior experience in financial services, banking, or credit risk modelling is beneficial. ...

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 ...

Expertise Hands-on architectural experience across enterprise and modern patterns, including: Cloud-native platforms (AWS, Azure, GCP). Snowflake, Microsoft Fabric and/or SageMaker (multi-platform experience valued). Data lakes and lakehouse architectures . Event-driven and streaming architectures . Data Mesh/Data Fabric principles , applied ...

Details • Salary: £70k–£90k + benefits • Working model: Hybrid (2 days onsite, London or Manchester) • Tech stack: Python, SQL, LLMs, AWS/GCP (SageMaker, Bedrock, etc.) • Visa: Cannot sponsor Interested? Please apply below. ...

through research Job Qualifications We are looking for experience in the following skills and experience: AI/ML platform technologies and services such as Sagemaker/Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices ...

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 ...

/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 ...

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 ...

best practices for AI/ML implementation and data analysis. Job Requirements: Advanced proficiency in Machine Learning and Data Science. Intermediate proficiency in Amazon Sagemaker. Experience with Retrieval-Augmented Generation, Software Development Life Cycle, and Model Context Protocol. Strong problem-solving skills and the ability to contribute ...

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 ...

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 ...