9 of 9 Amazon SageMaker Jobs in the City of 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 ...

/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 ...

with LLMs/GenAI frameworks (LangChain, RAG, prompt engineering, etc.) Experience in cloud (AWS/Azure/GCP) Experience in MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, etc.) API development experience ...

path to production - Integrate ML infrastructure with CI/CD systems and cloud platforms Required Skills & Qualifications: - Strong experience with ML platforms — MLflow, Kubeflow, SageMaker, or Vertex AI - Proficiency in Python and infrastructure tooling (Docker, Kubernetes, Terraform) - Experience building automated model training and deployment pipelines - Familiarity with feature stores ...

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 ...

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 ...

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. ...

core ML libraries (PyTorch, TensorFlow, scikit-learn) - Experience building and deploying LLM integrations or fine-tuned models - Familiarity with model serving infrastructure (e.g., SageMaker, Vertex AI, BentoML) - Solid understanding of vector databases, embeddings, and retrieval systems - Ability to communicate technical findings clearly to non-technical stakeholders Equal Opportunity Employer ...

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 ...