1 to 25 of 118 Permanent Amazon SageMaker Jobs

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

validation techniques. Solid programming skills in Python with familiarity in Bash, C, and C++. Practical experience deploying machine learning models in production environments. Familiarity with AWS services such as SageMaker, Bedrock, and GlueDemonstrated ability to work with large-scale datasets using tools such as Pandas, NumPy, and SQL. U.S. citizenship required Active TS/SCI w. Polygraph clearance required More ❯

ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end More ❯

ability to write and manage quality tickets and merge requests Preferred Qualifications: Experience working with the DoD and/or Federal government agencies Experience with MLOps tools (e.g., MLflow, SageMaker, Vertex AI). Background in deep learning, computer vision, or natural language processing. Contributions to open-source ML projects or publications in ML research The ideal candidate will combine More ❯

in Python, Java, or C++ Familiarity with TensorFlow, PyTorch, Hugging Face, Scikit-learn, and related ML libraries Experience with Docker, Kubernetes, and cloud AI platforms such as AWS Bedrock, Sagemaker, Azure ML, or GCP Vertex AI Working knowledge of data tools such as Spark, Pandas, SQL/NoSQL databases Security: TS clearance required Nice to Have Experience with LangChain More ❯

React, Angular, DevOps, CI/CD, Git, microservices, REST API, Kafka, Kinesis, distributed systems, RAG pipelines, LangChain, Bedrock, Hugging Face, Qdrant, computer vision, deep learning, neural networks, TensorFlow, PyTorch, SageMaker, Bedrock, security clearance, Top Secret, Secret, DevSecOps, containerization, automation, data engineering, cloud architecture, scalable systems, federal contracting, cleared engineer More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

machine learning models in production environments. Strong knowledge of Python and common ML frameworks such as scikit-learn, PyTorch, or TensorFlow. Experience with MLOps practices and tools (e.g., MLflow, SageMaker, Vertex AI, DVC). Familiarity with financial data models, structured datasets, and data validation. Understanding of regulatory reporting, trade lifecycle data, or capital markets workflows. Experience with cloud platforms More ❯

experience in AI/ML engineering, with proven experience in LLM or chatbot development. Strong proficiency in Python (LangChain, or similar frameworks). Experience with Databricks, Azure ML, AWS SageMaker, or equivalent platforms. Hands-on experience with OpenAI API, Anthropic Claude, Hugging Face Transformers, or MosaicML. Familiarity with vector databases. Solid understanding of prompt engineering, RAG, model fine-tuning More ❯

LangChain, LangGraph) • Familiarity with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and NLP libraries (e.g., spaCy, Hugging Face Transformers) • Hands-on with MLOps or model-serving tools (e.g., MLflow, SageMaker, Kubeflow) • Familiarity with observability stacks (Prometheus/Grafana preferred; CloudWatch, ELK/EFK acceptable) • Experience with event-driven and streaming systems (Kafka, Kinesis, SQS/SNS, AWS Step Functions More ❯

of experience (4 years may be used in lieu of a bachelors degree, 6 years may be used in lieu of a asters degree) Knowledge of AWS MLSpace, AWS SageMaker and AWS Bedrock for AI and machine learning integration Strong experience with cloud-native development and tools, including AWS Lambda, EC2, S3, and other AWS services. Proficiency in Python More ❯

to communicate technical solutions clearly to non-technical stakeholders Technical skills (a big plus): Knowledge of deep learning frameworks (PyTorch, TensorFlow), transformers, or LLMs Familiarity with MLOps tools (MLflow, SageMaker, Airflow, etc.) Experience with streaming data (Kafka, Kinesis) and distributed computing (Spark, Dask) Skills in data visualization apps (Streamlit, Dash) and dashboarding (Tableau, Looker) Domain experience in forecasting, optimisation More ❯

to communicate technical solutions clearly to non-technical stakeholders Technical skills (a big plus): Knowledge of deep learning frameworks (PyTorch, TensorFlow), transformers, or LLMs Familiarity with MLOps tools (MLflow, SageMaker, Airflow, etc.) Experience with streaming data (Kafka, Kinesis) and distributed computing (Spark, Dask) Skills in data visualization apps (Streamlit, Dash) and dashboarding (Tableau, Looker) Domain experience in forecasting, optimisation More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments, working with large datasets and parallelized processing. Can communicate technical concepts and trade-offs to both technical and More ❯

a Security+ CE, SSCP, CCNA-Security, or GSEC certification within 6 months of hire Nice If You Have: Experience developing prompts, tools, and agents with LLMs Experience with AWS SageMaker, Lambda, API Gateway, DynamoDB, S3, and IAM Experience with Kubernetes Experience with IaC tools like Helm and Terraform Experience with design and implementation, including building, containerizing, and deploying end More ❯

i.e. Apache Spark, Scikit-learn, XGBoost, etc.) Data manipulation and pipeline libraries (i.e. Pandas, Polars, matplotlib, Plotly, numpy, scipy, etc.) Data science environments (e.g. Jupyter Notebook, Data Bricks, or Amazon Sage Maker) Experience implementing the data science process, developing experiments, reporting and explaining results Experience with Python and/or Java programming languages Experience designing, developing, documenting, testing, and More ❯