Amazon Web Services (AWS)
Senior Delivery Consultant - Senior Machine Learning Engineer, AWS Professional
Amazon Web Services (AWS), Chicago, Illinois, United States, 60290
Overview
Senior Delivery Consultant - Senior Machine Learning Engineer, AWS Professional Services, AWS Professional Services Join to apply for the Senior Delivery Consultant - Senior Machine Learning Engineer, AWS Professional Services, AWS Professional Services role at Amazon Web Services (AWS). Responsibilities
Leading project teams and implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring. Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads. Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable. Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models. Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures. Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts. Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies. This is a customer-facing role with potential travel to customer sites as needed. About the Team
About AWS: AWS values diverse experiences. If your career is not linear or you do not meet all of the preferred qualifications, we encourage you to apply. AWS is the world’s most comprehensive and broadly adopted cloud platform, trusted by customers from startups to Global 500 companies. Inclusive Team Culture, Mentorship & Career Growth, and Work/Life Balance are emphasized in AWS culture to support employee development and well-being. Basic Qualifications
5+ years cloud architecture and implementation Bachelor's degree in Computer Science, Engineering, related field, or equivalent experience 8+ years leading technical teams and hands-on experience focused on data, software, or ML engineering, with strong understanding of distributed computing (e.g., data pipelines, training and inference, ML infrastructure design) 5+ years developing predictive modeling, natural language processing, and deep learning, with a proven track record of building and deploying ML models on cloud (e.g., Amazon SageMaker or similar) 5+ years developing with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript). Proficient with leading ML libraries and frameworks (e.g., TensorFlow, PyTorch) Preferred Qualifications
AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, Step Functions, VPC, CloudFormation) AWS Professional certifications (e.g., Solutions Architect Professional, DevOps Engineer Professional) Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., CloudFormation, CDK), and Containers & CI/CD Pipelines. Knowledge of common security and compliance standards (e.g., HIPAA, GDPR) Strong communication skills with ability to explain complex concepts to technical and non-technical audiences and the ability to lead technical teams in customer projects Experience building ML pipelines with MLOps best practices, including data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed & GPU training, deployment, monitoring, and retraining Experience with MLOps (e.g., MLFlow, Kubeflow) and orchestration (e.g., Airflow, AWS Step Functions). Experience building applications using GenAI technologies (LLMs, Vector Stores, LangChain, Prompt Engineering) Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you need a workplace accommodation or adjustment during the application or hiring process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $138,200/year in our lowest geographic market up to $239,000/year in our highest geographic market. Pay is based on factors including market location and experience. Amazon is a total compensation company; depending on the position, equity, sign-on payments, and other forms of compensation may be provided as part of a total package. For more information, visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. Company
- Amazon Web Services, Inc. Job ID: A2988778 Experience Level
Mid-Senior level Employment Type
Full-time Job Function
Research, Science, and Engineering Industries: IT Services and IT Consulting Note: This reformatted posting removes duplicates and standardizes structure while preserving the original content and intent.
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Senior Delivery Consultant - Senior Machine Learning Engineer, AWS Professional Services, AWS Professional Services Join to apply for the Senior Delivery Consultant - Senior Machine Learning Engineer, AWS Professional Services, AWS Professional Services role at Amazon Web Services (AWS). Responsibilities
Leading project teams and implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring. Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads. Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable. Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models. Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures. Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts. Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies. This is a customer-facing role with potential travel to customer sites as needed. About the Team
About AWS: AWS values diverse experiences. If your career is not linear or you do not meet all of the preferred qualifications, we encourage you to apply. AWS is the world’s most comprehensive and broadly adopted cloud platform, trusted by customers from startups to Global 500 companies. Inclusive Team Culture, Mentorship & Career Growth, and Work/Life Balance are emphasized in AWS culture to support employee development and well-being. Basic Qualifications
5+ years cloud architecture and implementation Bachelor's degree in Computer Science, Engineering, related field, or equivalent experience 8+ years leading technical teams and hands-on experience focused on data, software, or ML engineering, with strong understanding of distributed computing (e.g., data pipelines, training and inference, ML infrastructure design) 5+ years developing predictive modeling, natural language processing, and deep learning, with a proven track record of building and deploying ML models on cloud (e.g., Amazon SageMaker or similar) 5+ years developing with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, TypeScript). Proficient with leading ML libraries and frameworks (e.g., TensorFlow, PyTorch) Preferred Qualifications
AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, Step Functions, VPC, CloudFormation) AWS Professional certifications (e.g., Solutions Architect Professional, DevOps Engineer Professional) Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., CloudFormation, CDK), and Containers & CI/CD Pipelines. Knowledge of common security and compliance standards (e.g., HIPAA, GDPR) Strong communication skills with ability to explain complex concepts to technical and non-technical audiences and the ability to lead technical teams in customer projects Experience building ML pipelines with MLOps best practices, including data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed & GPU training, deployment, monitoring, and retraining Experience with MLOps (e.g., MLFlow, Kubeflow) and orchestration (e.g., Airflow, AWS Step Functions). Experience building applications using GenAI technologies (LLMs, Vector Stores, LangChain, Prompt Engineering) Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. If you need a workplace accommodation or adjustment during the application or hiring process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $138,200/year in our lowest geographic market up to $239,000/year in our highest geographic market. Pay is based on factors including market location and experience. Amazon is a total compensation company; depending on the position, equity, sign-on payments, and other forms of compensation may be provided as part of a total package. For more information, visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. Company
- Amazon Web Services, Inc. Job ID: A2988778 Experience Level
Mid-Senior level Employment Type
Full-time Job Function
Research, Science, and Engineering Industries: IT Services and IT Consulting Note: This reformatted posting removes duplicates and standardizes structure while preserving the original content and intent.
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