AWS AI/ML Engineer — Production ML on AWS

Robotic Technologies Automation; City of Albany (NY); On-site; USD 84,000 - 120,000; Full time

Job summary

Robotic Technologies Automation in Albany, NY, seeks an experienced AWS 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 secure, scalable deployment—and partner with data scientists, platform, and security teams.

You will design end‑to‑end ML pipelines on SageMaker, Bedrock, ECS, Lambda, and Step Functions, implement IaC and CI/CD for ML workloads, and ensure robust monitoring, security, and cost

Qualifications

• 4+ years of AI/ML and cloud engineering with hands-on AWS
• Strong experience with AWS ML services and infrastructure
• Experience building CI/CD pipelines for ML workloads
• Solid understanding of ML lifecycle, monitoring, and security practices
• Proficiency in Python and containerization

Responsibilities

• Design end-to-end ML pipelines on AWS for training, evaluation, deployment, and inference
• Implement IaC and CI/CD pipelines for ML workloads
• Build robust model monitoring, drift detection, logging, and observability solutions
• Optimize cost, performance, and reliability of training and inference workloads
• Establish secure AI/ML practices: least-privilege IAM, encryption, network controls
• Collaborate with data scientists to productionize models and improve reproducibility
• Maintain model registries, feature stores, experiment tracking, and artifact lineage
• Document architecture and runbooks; participate in on-call rotation for prod ML systems

Skills

Python

CI/CD experience

Tools

AWS SageMaker

AWS Bedrock

EC2

ECS/EKS

S3

IAM

VPC

CloudWatch

Lambda

Step Functions

Terraform

CloudFormation

GitHub Actions

GitLab CI

CodePipeline

Docker

Kubernetes

Job description

Robotic Technologies Automation in Albany, NY, seeks an experienced AWS 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 secure, scalable deployment—and partner with data scientists, platform, and security teams.

You will design end‑to‑end ML pipelines on SageMaker, Bedrock, ECS, Lambda, and Step Functions, implement IaC and CI/CD for ML workloads, and ensure robust monitoring, security, and cost