AWS AI/ML Engineer — Production ML Pipelines

Robotic Technologies Automation; Sunnyvale (CA); On-site; USD 88,000 - 125,000; Full time

Job summary

Robotic Technologies Automation is seeking an experienced AI/ML Engineer to design, build, and operate production-grade ML systems on AWS. You will own the full ML lifecycle—from experiment tracking to deployment, monitoring, and continuous improvement—while partnering with data scientists, platform, and security teams.

Key responsibilities include building end-to-end ML pipelines on AWS, implementing infrastructure-as-code and CI/CD for ML workloads, and establishing robust monitoring, cost

Qualifications

• 4+ years of experience in AI/ML and cloud platform engineering with significant hands-on AWS work.
• 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 (real-time, batch, streaming), and monitoring for data/model drift.
• Proficiency in Python and containerization (Docker, Kubernetes/EKS/ECS).
• Familiarity with security best practices for ML systems (IAM least privilege, encryption, network controls, audit logging).

Responsibilities

• Design and maintain end-to-end ML pipelines on AWS (SageMaker, Bedrock, ECS, Lambda, Step Functions, etc.) for training, evaluation, deployment, and inference.
• Implement infrastructure-as-code (Terraform/CloudFormation) and CI/CD pipelines for ML workloads, including model packaging, versioning, and promotion across environments.
• Build robust model monitoring, drift detection, logging, and alerting solutions (CloudWatch, SageMaker Model Monitor, custom metrics).
• Optimize cost, performance, and reliability of training and inference workloads (spot instances, multi-instance training, autoscaling, batch vs. real-time endpoints).
• Establish secure AI/ML practices: least-privilege IAM, KMS encryption, network isolation, secret management, and compliance controls for model artifacts and data.
• Collaborate with data scientists to produce production grade models, improve reproducibility, and reduce time-to-deployment.
• Maintain model registries, feature stores, experiment tracking, and artifact lineage.
• Document architecture, runbooks, and operational procedures; participate in on-call rotation for production ML systems.

Skills

AI/ML

Cloud engineering

Python

CI/CD

Security

Tools

SageMaker

Bedrock

EC2

ECS

EKS

S3

IAM

VPC

CloudWatch

Lambda

Step Functions

Terraform

GitHub Actions

GitLab CI

Docker

Kubernetes

Job description

Robotic Technologies Automation is seeking an experienced AI/ML Engineer to design, build, and operate production-grade ML systems on AWS. You will own the full ML lifecycle—from experiment tracking to deployment, monitoring, and continuous improvement—while partnering with data scientists, platform, and security teams.

Key responsibilities include building end-to-end ML pipelines on AWS, implementing infrastructure-as-code and CI/CD for ML workloads, and establishing robust monitoring, cost