Cloud Engineer

Insight Global

New York (NY)

On-site

USD 100,000 - 150,000

Full time

14 days+

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Benefits offered by this job

Medical insurance

Vision insurance

401(k)

Job summary

A leading company seeks a Senior Cloud Engineer/DevOps Engineer with extensive hands-on experience in AWS infrastructure, particularly focusing on AI/ML solutions. The role involves collaborating across teams to develop and maintain machine learning models and implementing best practices in a cloud environment. Ideal candidates will have strong coding skills, experience with MLOps, and a proactive approach to problem-solving. This contract position offers competitive pay and opportunities for professional growth.

Qualifications

5+ years of experience in cloud engineering or DevOps.
Strong knowledge of AWS and MLOps.
Proficient in Python and Terraform.

Responsibilities

Act as the AI/ML subject matter expert across teams.
Collaborate with data science teams to maintain ML models.
Implement scalable ML solutions using Terraform and AWS.

Skills

AWS infrastructure

MLOps

Terraform

Python

Machine Learning lifecycle

Git and GitHub Actions

Agile methodologies

Job description

This range is provided by Insight Global. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$50.00/hr - $60.00/hr

Direct message the job poster from Insight Global

LOOKING FOR A SR CLOUD ENGINEER/DEVOPS ENGINEER WITH HANDS ON EXPERIENCE WITH AWS INFRASTRUCTURE FOCUSING ON AI/ML - local to NYC or Charlotte, NC!

Required Skills & Qualifications

5+ years of experience in cloud engineering or DevOps with deep knowledge of AWS infrastructure and services.
Strong hands-on experience with machine learning operations (MLOps) in cloud environments.
Proficient with Terraform for Infrastructure as Code (IaC). • Experience with AWS ML services like SageMaker, Step Functions, EKS, and Lambda.
Familiarity with the end-to-end ML lifecycle: data ingestion, model training, evaluation, deployment, and monitoring.
Strong coding and automation skills using Python.
Hands-on experience with MLflow or similar model tracking platforms.
Proficient with GitHub, GitHub Actions, and Agile tools like Jira.
Excellent communication and stakeholder management skills.

Key Responsibilities

Act as the AI/ML subject matter expert across teams, providing guidance, best practices, and hands-on support throughout the ML lifecycle.
Collaborate with data science and engineering teams to develop, deploy, and maintain ML models using AWS services such as SageMaker, Lambda, EKS, and Step Functions.
Architect and implement scalable, secure, and maintainable ML solutions in the AWS cloud environment using Infrastructure as Code (Terraform) and automation tools.
Support experimentation, tracking, and reproducibility using tools like MLflow.
Serve as a liaison between technical and non-technical teams, effectively communicating AI/ML strategies, timelines, and progress.
Contribute to CI/CD pipelines and ML operations using GitHub Actions and Python.
Apply Agile best practices in task management, sprint planning, and cross-functional collaboration.

Seniority level