Cox Enterprises

AI/ML Cloud Architect

Cox Enterprises

full-time

Posted on:

Location Type: Remote

Location: Remote • New York, North Carolina • 🇺🇸 United States

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Salary

💰 $131,600 - $219,400 per year

Job Level

Junior

Tech Stack

AWSAzureCloudOpen SourcePythonPyTorchScikit-LearnTensorflowTerraform

About the role

  • Design and develop Generative AI solutions using cloud-based managed AI services (e.g. Amazon Bedrock, Amazon SageMaker, Google Cloud Vertex AI).
  • Design and develop AI Agent workflows using cloud and open source agentic frameworks.
  • Containerize AI applications and deploy them using cloud orchestration services.
  • Collaborate with data architects/engineers to build end-to-end AI pipelines.
  • Implement MLOps practices to automate the development, deployment, and monitoring of AI applications and models.
  • Implement and manage robust monitoring systems for AI solutions in production environments, ensuring continuous performance tracking, anomaly detection, and model drift analysis; collaborate with cross-functional teams to deploy model updates, maintain version control, and optimize model efficiency over time.
  • Use Infrastructure as Code (IaC) to manage and version cloud resources for AI projects.
  • Ensure clear and accessible knowledge transfer to internal teams and create knowledge-sharing resources to ensure smooth transitions during model handoffs and system updates.
  • Contribute to the development of best practices and standards for AI engineering within the organization.

Requirements

  • Bachelor’s degree and 6 years’ experience in Architect/Engineer roles OR a Master’s degree and 4 years’ experience Architect/Engineer roles OR a Ph.D. and 1 years’ experience in Architect/Engineer roles OR 10 years’ experience in Architect/Engineer roles.
  • Experience using Python programming language to create and integrate GenAI solutions related to business workloads
  • 2 years of experience of managing AI services within one cloud platform (AWS preferred, Google Cloud, Azure)
  • Experience with container services and orchestration (AWS EKS, ECS, GKE, etc.)
  • Experience in common machine learning, deep learning, and LLM frameworks, such as TensorFlow, PyTorch, scikit-learn, Hugging Face Transformers, LangChain, LangGraph.
  • 2 years experience with using Terraform for managing Infrastructure as Code (IaC) as well as CI/CD deployments.
  • Experience in a client-facing role (preferred)
  • In-depth knowledge of data services across major cloud platforms (AWS or Google Cloud) (preferred)
  • Professional certifications with focus on AI/ML from AWS, Google Cloud or Azure (preferred)
  • Experience with agentic workflows and streaming data processing (preferred)
  • Travel 15% of the time
  • Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship.
Benefits
  • the flexibility to take as much vacation with pay as they deem consistent with their duties, the company’s needs, and its obligations
  • seven paid holidays throughout the calendar year
  • up to 160 hours of paid wellness annually for their own wellness or that of family members
  • additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave
  • health care insurance (medical, dental, vision)
  • retirement planning (401(k))
  • paid days off (sick leave, parental leave, flexible vacation/wellness days, and/or PTO)
  • Flexible Work Option: Can work remotely anywhere in the specified country
  • Position may be eligible for additional compensation that may include an incentive program

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
PythonGenerative AIMLOpsInfrastructure as CodeTerraformmachine learningdeep learningLLM frameworkscontainerizationcloud orchestration
Soft skills
collaborationknowledge transfercommunicationclient-facing experiencebest practices development
Certifications
AI/ML certifications from AWSAI/ML certifications from Google CloudAI/ML certifications from Azure
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