AI/ML Engineer

Ethereum Technologies LLC; Austin (TX); On-site; USD 95,000 - 136,000; Full time

Job summary

Ethereum Technologies is seeking 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—collaborating with data scientists, platform, and security teams.

You will design end-to-end ML pipelines, implement IaC and CI/CD for ML workloads, and ensure robust monitoring, drift detection, and cost

Qualifications

• 4+ years of AI/ML and cloud platform engineering with hands-on AWS
• Experience with AWS ML services and infrastructure tooling

Responsibilities

• Design end-to-end ML pipelines on AWS (SageMaker, Bedrock, ECS, Lambda, Step Functions)
• Implement infrastructure-as-code and CI/CD pipelines for ML workloads
• Build model monitoring, logging, and drift detection
• Optimize cost, performance, and reliability of training/inference
• Maintain model registries, feature stores, and experiment tracking
• Document architecture and runbooks, participate in on-call rotation

Skills

AI/ML

Python

AWS

CI/CD

Docker

Kubernetes

Tools

SageMaker

Bedrock

Terraform

CloudWatch

ECS/EKS

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: AI/ML EngineerLocation: Austin, TX
Duration: 12 Months + Extension
Bill Rate: $84/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

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.