1 to 25 of 151 Permanent Amazon SageMaker Jobs

career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that … experience with model customization techniques such as fine-tuning, continued pre-training, and LLM-as-judge evaluation Experience with optimization of models on GPUs, Amazon Silicon, or TPUs, also experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools Experience ...

ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda, SageMaker), Kubernetes, Terraform/CDK ML Ops & Automation: MLFlow, feature stores, model monitoring, A/B testing Data Security & Compliance: Secure SDLC, ISO/NIST ...

. Strong experience with data manipulation and pipelines using Pandas, NumPy, and SQL. Knowledge of containerized deployments (Docker, Kubernetes) and cloud ML services (AWS SageMaker, GCP Vertex AI, or Azure ML) preferred. Excellent problem-solving skills, eagerness to learn, and ability to thrive in a fast-paced, evolving environment. ...

7+ years in software engineering, data science, or ML roles 3+ years deploying ML solutions in production Hands-on experience with AWS ML services (SageMaker, Bedrock, etc.) Experience with distributed training (Horovod, Ray, etc.) Proven work with generative AI and LLM applications Strong Python and ML framework skills (PyTorch ...

/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools ...

/CD pipelines, model versioning, and monitoring. Comprehensive experience with cloud platforms (AWS, Azure, or GCP) and cloud-native AI tools, such as SageMaker, Vertex AI, or Azure ML Studio. Ability to design and deploy cloud-based AI solutions. Proficiency in using containerization technologies (e.g., Docker) and orchestration tools ...

least one major cloud provider. AWS, GCP or Azure Familiar with Docker and CI/CD pipelines Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML Data engineering and MLOps Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like ...

least one major cloud provider. AWS, GCP or Azure Familiar with Docker and CI/CD pipelines Experience with managed ML platforms such as SageMaker, Vertex AI or Azure ML Data engineering and MLOps Experience with data warehouses such as Snowflake, BigQuery or Redshift Workflow orchestration using tools like ...

need: Strong Python and experience with TensorFlow, PyTorch or scikit-learn Hands-on LLM work—prompt engineering, modern NLP techniques AWS experience: SageMaker, Bedrock, S3, Lambda, CloudWatch Understanding of deep learning, transformers and attention mechanisms Knowledge of RAG systems, vector databases and embeddings Experience deploying ML models to production ...

AWS.Preferred Qualifications AWS certifications (e.g., AWS Certified Data Analytics Specialty, Solutions Architect Experience with EMR, Redshift, Kinesis, or Kafka. Knowledge of MLOps tools (SageMaker, MLflow, Feature Stores Familiarity with IaC (Terraform, CloudFormation Experience working in enterprise-scale, highly regulated environments.Soft Skills Strong communication and documentation skills for both technical ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...

with building ML services using Python web frameworks such as FastAPI or stream processing libraries like Faust. Experience using tools like Jupyter Notebooks, AWS SageMaker, and AWS Bedrock. Hands-on expertise with Kafka and vector databases. Experience managing ML lifecycle workflows with MLflow. Deep understanding of LLMs and generative ...