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# AI/ML Engineer

- **Company:** Quantum Technologies
- **Location:** Atlanta, GA, United States
- **Experience:** Experienced
- **Salary:** $143,520
- **Contract:** Contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Audit Trail, Digital Architecture, Github, Identity and Access Management, Python (Programming Language), Data Streaming, Management of Software Versions, Data Logging, Cloud Platform System, Autoscaling, Delivery Pipeline, State Machines, Amazon Virtual Private Cloud (VPC), Cloudformation, Containerization, Gitlab-ci, Kubernetes, Machine Learning Operations, Functional Programming, Drift Detection, Cloudwatch, Terraform, Software Version Control, Docker
- **Published:** November 12, 2026
- **Apply:** https://www.dice.com/job-detail/e5bcf7d4-151b-41a2-aed6-b6de560acdc8

## About the Role

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 experience with containerization (Docker, Kubernetes/EKS/ECS).
Familiarity with security best practices for ML systems (IAM least privilege, encryption, network controls, audit logging).

## Description

We are looking for an experienced AI/ML Engineer to design, build, and operate production-grade machine learning systems on AWS. You will own the full ML lifecycle-from experiment tracking and model training to secure, scalable deployment, monitoring, and continuous improvement-while partnering closely with data scientists, platform, and security teams., 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 ing 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.