AWS AI/ML Engineer

Quantum Technologies. LLC; Austin (TX); On-site; USD 96,000 - 124,000; Full time

Job summary

Quantum Technologies, LLC in Austin, TX is seeking an experienced AWS AI/ML Engineer for an 8-month engagement. You will design, build, and operate production-grade ML systems on AWS, owning the full lifecycle from experimentation to secure deployment and monitoring.

Collaborate with data scientists and security teams to implement scalable pipelines using SageMaker, Bedrock, ECS, and Lambda, and to implement CI/CD and IaC practices.

Qualifications

• 4+ years of AI/ML and cloud platform engineering with significant hands-on AWS work.
• Experience with AWS ML services (SageMaker, Bedrock) and infrastructure-as-code.
• Proficiency in Python and containerization (Docker/Kubernetes).

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 model monitoring and logging solutions and optimize training/inference cost.
• Collaborate with data scientists and security teams to ensure reproducibility and secure deployments.

Skills

Python programming

AWS cloud services

ML lifecycle

Containerization

Security best practices

Cost optimization

Tools

Terraform

CloudFormation

Docker

Kubernetes

SageMaker

CodePipeline

Job description

Job Title: AWS AI/ML Engineer

Location: Austin, TX

Duration: 8 Months + Extension

Bill Rate: $78/hour (C2C)

Job Type: W-2, 1099, C2C

Client: To Be Discussed Later

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Description

Role Summary

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.

Key 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 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.

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

Preferred Qualifications

• Experience with SageMaker Pipelines, Feature Store, Model Registry, or Bedrock for generative AI workloads.
• Knowledge of additional MLOps tools (MLflow, Kubeflow, Weights & Biases, or similar).
• Background in ML security, red teaming, or secure MLOps (guardrails, prompt injection mitigation, model supply-chain security).
• Experience with cost optimization, observability platforms, or large-scale training/inference systems.
• Contributions to open-source MLOps or AWS tooling; relevant certifications (AWS ML Specialty, DevOps Engineer Professional, or Security Specialty)

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.