Amazon Web Services (AWS)
Senior Delivery Consultant AI/ML, AWS Professional Services
Amazon Web Services (AWS), Denver, Colorado, United States, 80285
Senior Delivery Consultant AI/ML, AWS Professional Services
Join to apply for the
Senior Delivery Consultant AI/ML, AWS Professional Services
role at
Amazon Web Services (AWS) . As a Delivery Consultant, you will lead project teams and implement end‑to‑end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring. 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. 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 with 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. This position will remain posted until filled.
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Join to apply for the
Senior Delivery Consultant AI/ML, AWS Professional Services
role at
Amazon Web Services (AWS) . As a Delivery Consultant, you will lead project teams and implement end‑to‑end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring. 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. 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 with 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. This position will remain posted until filled.
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