AWS AI/ML Engineer: Full ML Lifecycle

Ethereum Technologies; Sunnyvale (CA); On-site; USD 90,000 - 123,000; Full time

Job summary

Ethereum Technologies in Sunnyvale, CA seeks an experienced AI/ML Developer to design, build, and operate production-grade machine learning systems on AWS. You will own the full ML lifecycle—from experiment tracking to secure deployment and monitoring—working closely with data scientists, platform, and security teams.

The role emphasizes end-to-end ML pipelines (SageMaker, Bedrock, ECS), infrastructure-as-code (Terraform/CloudFormation), and robust monitoring.

Qualifications

• 4+ years of AI/ML and cloud platform engineering with hands-on AWS.
• Strong proficiency with AWS ML and infrastructure services (SageMaker, Bedrock, EC2, ECS/EKS, S3, IAM, VPC, CloudWatch, Lambda, Step Functions).
• Experience building and operating CI/CD pipelines for ML (CodePipeline, GitHub Actions, GitLab CI, or equivalent) and infrastructure-as-code (Terraform preferred).
• Solid understanding of ML lifecycle concepts: experiment tracking, model versioning, serving patterns, and monitoring for drift.

Responsibilities

• Design and maintain end-to-end ML pipelines on AWS for training, evaluation, deployment, and inference.
• Implement infrastructure-as-code and CI/CD pipelines for ML workloads across environments.
• Build model monitoring, drift detection, logging, and alerting solutions.
• Optimize cost, performance, and reliability of training and inference workloads.

Skills

Python programming

AWS cloud

CI/CD automation

Containerization

Tools

SageMaker

Bedrock

Terraform

CloudFormation

Docker

Kubernetes

CodePipeline

GitHub Actions

Job description

Ethereum Technologies in Sunnyvale, CA seeks an experienced AI/ML Developer to design, build, and operate production-grade machine learning systems on AWS. You will own the full ML lifecycle—from experiment tracking to secure deployment and monitoring—working closely with data scientists, platform, and security teams.

The role emphasizes end-to-end ML pipelines (SageMaker, Bedrock, ECS), infrastructure-as-code (Terraform/CloudFormation), and robust monitoring.