Senior Delivery Consultant - AI/ML, AWS Professional Services
Amazon Web Services (AWS) · Herndon, VA · 3 wk ago
EngineeringFull-time
About the role
The Amazon Web Services Professional Services (ProServe) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant 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
Requirements
- Bachelor's degree in business administration, finance, economics, computer science, data science, engineering, or other related field
- Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience
- 5+ years of cloud architecture and solution implementation experience
- 5+ years of development/programming/scripting language (Python/Java/Bash/Perl) experience
- 5+ years leading technical teams and hands-on experience focused on data, software, or ML engineering, with 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 experience in building and deploying ML models on cloud (e.g., Amazon SageMaker or similar)