76 to 100 of 135 Permanent Amazon SageMaker Jobs

inference Advanced Python skills (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) Experience building, deploying and optimising ML models in production Strong AWS experience (e.g. SageMaker, Lambda or similar services) Expert SQL skills and experience working with large, complex datasets Solid data engineering fundamentals, including pipelines and APIs Comfortable with ...

scikit-learn, PyTorch, transformers) Experience deploying models and working with cloud platforms (AWS preferred) Ability to communicate complex technical concepts clearly Desirable AWS services (SageMaker, ECS, Lambda, S3, Bedrock) MLOps/ML engineering experience (Docker, MLflow) Experience in government or regulated environments Practical experience applying LLMs Security Clearance UKIC ...

Computer Science, Machine Learning, Engineering , or a closely related discipline. Bonus Points Experience with MLOps and model serving platforms such as BentoML, MLflow, or SageMaker . Hands-on work with streaming and batch data pipelines using tools like Spark, Airflow, or Apache Beam . Background in front ...

marketplace or matching algorithms. Exposure to flexibility markets, virtual power plants, or reconciliation/settlement processes. Experience with ML deployment frameworks (MLflow, Vertex AI, SageMaker). Knowledge of optimisation libraries such as Gurobi, OR‐Tools, or Pyomo. ...

marketplace or matching algorithms. Exposure to flexibility markets, virtual power plants, or reconciliation/settlement processes. Experience with ML deployment frameworks (MLflow, Vertex AI, SageMaker). Knowledge of optimisation libraries such as Gurobi, OR‐Tools, or Pyomo. ...

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 ...

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 ...

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 ...

PyTorch Experience with containerization tools like Docker and orchestration tools like Airflow or Astronomer Familiarity with cloud platforms (AWS, Azure) and ML services (e.g., SageMaker, Vertex AI) Experience with CI/CD pipelines and automation tools such as GitHub Actions Understanding of monitoring and logging tools (e.g., NewRelic, Grafana ...

expert should have: A strong academic background in data science, computer science, mathematics, or a related field. Hands-on experience with AWS ML stack (SageMaker, Lambda, Redshift). Proven ability to design and implement machine learning algorithms and models. Proficiency in Python, SQL, and ML libraries (e.g., scikit-learn ...

technical stakeholders Mentor junior team members Skillset: Strong Python and ML libraries (scikit-learn, PyTorch, pandas, NumPy) Experience with AWS cloud services (SageMaker, Lambda, S3) Familiarity with MLOps tools (MLflow, Weights & Biases) Knowledge of NLP, computer vision, or time-series analysis Ability to design experiments and apply statistical validation ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...

designing, supporting, and operating ML systems in production Technical environment (experience expected across most of these) MLflow (expert-level) Databricks Cloud platforms (AWS preferred; SageMaker exposure a bonus) CI/CD for ML workloads Docker and Kubernetes Infrastructure as Code (Terraform or similar) Python-based ML workflows ...