16 of 16 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 ...

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 ...

/CD pipelines. You can set up APIs, write robust code, and debug pipelines. ● Experience with cloud-native ML workflows, including AWS Sagemaker, Lambda, ECS, and containerisation with Docker. ● Familiarity with frontend collaboration—how your model outputs can drive dynamic UIs, content personalization, or editor tools. ● Comfort in “hacking ...

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 ...

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 ...

Python and SQL skills Understanding of data platforms (eg Snowflake/CDP) and ingestion pipelines Exposure to AI/ML enablement frameworks (.eg AWS SageMaker) and supporting infrastructure for model training, deployment Experience with dbt/ELT patterns (beneficial, not core focus) Strong stakeholder communication and technical leadership skills ...

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 ...

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 ...

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 ...

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. ...

/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 ...