Talantix.io
Bright Vision Technologies•08.02.26
AI SCORE 8.5

AI Infrastructure Engineer - Remote Position

$120K–$150K/year
RemoteAI/Big DataVisa
AI Infrastructure•Python•Linux•Kubernetes•Docker•AWS•Azure•GCP•NVIDIA CUDA•Terraform•CI/CD

About the Role

We are seeking an experienced AI Infrastructure Engineer to join Bright Vision Technologies, a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. This AI Infrastructure Engineer remote position offers a fantastic opportunity to contribute to our mission of transforming business processes through technology.

What You'll Do

  • Design and implement AI infrastructure solutions that support machine learning and AI workloads.
  • Utilize cloud technologies such as AWS, Azure, and GCP to build scalable and secure platforms.
  • Develop and maintain CI/CD pipelines for efficient deployment and monitoring of AI applications.
  • Work with GPU computing technologies, particularly NVIDIA CUDA, to optimize performance.
  • Collaborate with cross-functional teams to ensure infrastructure aligns with business needs.

Requirements

  • 3-5 years of experience as an AI Infrastructure Engineer remote.
  • Proficient in Python, Linux, Kubernetes, and Docker.
  • Experience with AI workload orchestration and high-performance computing.
  • Strong understanding of distributed systems and networking for AI.
  • Familiarity with Infrastructure as Code (Terraform) and Agile methodologies.

Nice to Have

  • Experience in monitoring and observability tools.
  • Knowledge of Git for version control.
  • Previous work in a remote environment.

What We Offer

  • Competitive salary range of $120,000 - $150,000 per year.
  • Opportunities for career growth and development.
  • Remote work flexibility to enhance work-life balance.
  • Support for H-1B visa sponsorship for eligible candidates.
  • A commitment to fostering an inclusive and diverse work environment.
Language Requirements
EnglishB2
BasicIntermediateAdvancedNative
Why This Job8.5 of 10

Join Bright Vision Technologies as an AI Infrastructure Engineer and work remotely while contributing to innovative AI solutions. Enjoy competitive pay and career growth opportunities.

Salary Range
Required
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Optional
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Bonus
0/1

Who Will Succeed Here

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Proficient in deploying and managing AI workloads using Kubernetes and Docker, ensuring seamless integration and scalability of AI models in a cloud environment such as AWS or GCP.

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Self-motivated and disciplined in a remote work setting, capable of managing time effectively and collaborating asynchronously with cross-functional teams to drive projects forward.

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Strong analytical mindset with hands-on experience in optimizing infrastructure performance using tools like Terraform and NVIDIA CUDA to enhance AI model efficiency.

Learning Resources

→Kubernetes for AI - A Practical Guideguide
→AWS Certified Solutions Architect - Associate 2023course
→Docker and Kubernetes: The Complete Guidecourse

Career Path

AI Infrastructure Engineer(Now)→AI Infrastructure Lead(1-2 years)→Director of AI Infrastructure(3-5 years)

Market Overview

Market Size 2024
$15.5B
Annual Growth
22.3%
AI Adoption
75%
Investment in AI Infrastructure
+150%
Labour Demand for AI Infrastructure Roles
+30%
Avg Salary for AI Infrastructure Engineer
$120K

Skills & Requirements

Required
AI InfrastructurePythonLinux
Growing in Demand
Machine Learning Operations (MLOps)Data EngineeringServerless Computing
Declining
Traditional Data Center ManagementOn-Premises Infrastructure Management

Domain Trends

Increased Demand for Scalable AI Solutions
With AI adoption reaching 75%, companies are seeking scalable AI infrastructure solutions to support diverse workloads, leading to a 30% increase in job postings for AI Infrastructure roles.
Shift Towards Multi-Cloud Strategies
Organizations are increasingly adopting multi-cloud strategies, with 70% of enterprises using two or more public clouds, which drives demand for engineers skilled in AWS, Azure, and GCP.
Automation in AI Infrastructure Management
The rise of automation tools has led to a 40% reduction in manual infrastructure management tasks, pushing the need for skills in Terraform and Kubernetes for efficient AI deployment.

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