AI/ML Engineer (AWS)

Quantum Technologies LLC; Sunnyvale (CA); On-site; USD 83,000 - 131,000; Full time

Job summary

Quantum Technologies. LLC 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—while partnering closely with data scientists, platform, and security teams.

Responsibilities include designing end-to-end ML pipelines on AWS, implementing IaC and CI/CD, building monitoring and cost-optimization, and ensuring secure practices across model artifacts and data.

Qualifications

• 4+ years of AI/ML and cloud platform engineering with hands-on AWS experience.
• Proficiency with AWS services (SageMaker, Bedrock, EC2, ECS/EKS, S3, IAM, VPC, CloudWatch, Lambda, Step Functions).
• Experience building CI/CD pipelines for ML and infrastructure-as-code (Terraform preferred).
• Solid understanding of ML lifecycle concepts: experiment tracking, model versioning, serving patterns, and monitoring for 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 for training, evaluation, deployment, and inference.
• Implement infrastructure-as-code 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 Engineering

AWS Cloud

Python

Docker

Kubernetes

CI/CD

Security Best Practices

Tools

Terraform

CloudFormation

SageMaker

Bedrock

EC2

ECS/EKS

S3

IAM

VPC

CloudWatch

Job description

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

Job Title: AI/ML Developer (AWS)

Location: Sunnyvale,CA

Duration: 10 Months

Bill Rate: $77/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 Discription:

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 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.

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.