Senior AI/GenAI Delivery Consultant — Cloud Solutions

Amazon Web Services (AWS); Chicago (IL); On-site; USD 154,000 - 208,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon Web Services, Inc. (AWS) Professional Services is seeking a skilled ML Engineer to join our Delivery Consultant team. You will design, implement, and manage AWS AI/ML and GenAI solutions that meet customer requirements and business objectives.

You will lead customer-facing project teams, architect scalable, secure solutions, and ensure successful delivery through the ML lifecycle with best practices and risk management across engagements.

Qualifications

• Bachelor's degree in CS, engineering, or related field.
• 5+ years cloud architecture and solution implementation experience.
• 5+ years development/programming/scripting (Python/Java/Bash/Perl).
• 5+ years leading technical teams with data/ML engineering.
• 5+ years building ML models on cloud platforms (SageMaker or similar).

Responsibilities

• Lead project teams and end-to-end AI/ML/GenAI projects from requirements to deployment.
• Design scalable ML pipelines for high-performance workloads.
• Implement MLOps with AWS services and GenAI when applicable.
• Collaborate with Applied Science, DevOps, Data Eng, and Cloud Infra teams.
• Mentor team members and share best practices across the organization.

Skills

Cloud architecture

Solution implementation

Programming languages

Leading technical teams

ML model deployment

Distributed computing

SageMaker experience

Education

Bachelor's degree in CS/Engineering

Tools

Terraform

CloudFormation

CDK

Kubernetes

MLFlow

Kubeflow

Airflow

Job description

Amazon Web Services, Inc. (AWS) Professional Services is seeking a skilled ML Engineer to join our Delivery Consultant team. You will design, implement, and manage AWS AI/ML and GenAI solutions that meet customer requirements and business objectives.

You will lead customer-facing project teams, architect scalable, secure solutions, and ensure successful delivery through the ML lifecycle with best practices and risk management across engagements.