9 of 9 Contract Amazon SageMaker Jobs

validation, bias/fairness testing, explainable AI (XAI) techniques, and performance monitoring. Qualifications & Experience (Preferred) Publications in recognized AI/ML journals or conferences. Experience with cloud platforms (AWS SageMaker, Google Vertex AI, Azure ML). Knowledge of graph databases and network analysis techniques. Experience with real-time model inference and API development (e.g., FastAPI, Flask). Personal Attributes More ❯

agent solutions that integrate with enterprise systems and cloud platforms. Develop and optimize RESTful and GraphQL APIs to facilitate AI-driven interactions. Utilize AWS services (Lambda, S3, API Gateway, SageMaker, DynamoDB, ECS, etc.) to deploy scalable AI solutions. Implement Full Stack JavaScript (Node.js, React.js, Express, TypeScript, Next.js, etc.) applications to support AI-driven interfaces. Work closely with data scientists More ❯

gates. AI/ML Infrastructure Security: Harden and secure the underlying cloud infrastructure for AI/ML workloads, including GPU clusters, container orchestration (Kubernetes), and managed services (e.g., AWS SageMaker, Azure ML). Security by Design: Embed security controls into every stage of the ML lifecycle (data ingestion, feature store, model training, deployment, monitoring). Implement secrets management, network More ❯

on cutting-edge machine learning and data engineering projects, leveraging the latest cloud technologies and MLOps practices. Required/Primary Skills: AWS Data Engineering ML Engineering ML-Ops ECS, Sagemaker Gitlab Jenkins CI/CD AI Lifecycle Experience in front-end development (HTML, Stream-lit, Flask Familiarity with model deployment and monitoring in cloud environments (AWS). Understanding of More ❯

with the organisation's existing team to support the delivery of one of the organisation's key strategic initiatives. Experience required: AWS Data/ML Engineering & ML Ops (ECS, Sagemaker) CI/CD pipelines (GitLab, Jenkins) Python, PySpark & Big Data ecosystems AI/ML lifecycle, deployment & monitoring MLOps tooling (MLflow, Airflow, Docker, Kubernetes) Front-end exposure (HTML, Flask, Streamlit More ❯

Claude, Gemini, Mistral, LLaMA, Cohere). Experiment with advanced prompting techniques; Chain-of-Thought, ReAct, Tree-of-Thoughts, and more. Deploy AI/ML pipelines using Azure ML, AWS SageMaker, Vertex AI, or Databricks. Integrate LLMs into production apps using LangChain, LlamaIndex, and RAG architectures. Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like … related field. Tech Stack LLMs: GPT-4/5, Claude, Gemini, Mistral, LLaMA, Cohere Frameworks: LangChain, LlamaIndex, Haystack Tools: GitHub Copilot, Cursor, PromptLayer, Weights & Biases Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks Infra: Python, Docker, Kubernetes, SQL, PyTorch More ❯

We're partnering with an established AI consultancy seeking a Contract Machine Learning Engineer (NLP) to lead an NLP project for a major utilities client. You'll develop and deploy transformer models, LLMs, and RAG pipelines to extract insights from More ❯

Inside IR35 1-2 days in London office We need a Lead data engineer who has hands on experience as a Data Architect: You musty have in: AWS Glue Sagemaker Architect data platforms while having hands on experience Please send me copy of your job CV if you're interested More ❯

Inside IR35 1-2 days in London office We need a Lead data engineer who has hands on experience as a Data Architect: You musty have in: AWS Glue Sagemaker Architect data platforms while having hands on experience Please send me copy of your job CV if you're interested More ❯