Talantix.io
Katonic AI•06.02.26
AI SCORE 8.5

AI Infrastructure Product Engineer - Remote

$90K–$120K/year
RemoteAI/Big Data
AI Infrastructure•Product-management•AWS•Azure•GCP•Docker•Kubernetes

About the Role

We are seeking an experienced AI Infrastructure Product Engineer to join our dynamic team at Katonic AI. This remote position offers a unique opportunity to work on cutting-edge AI infrastructure projects that drive innovation and efficiency. You will play a critical role in shaping the future of AI technologies and their applications in various industries.

What You'll Do

  • Collaborate with cross-functional teams to define product requirements and specifications for AI infrastructure solutions.
  • Design and implement scalable AI infrastructure that supports machine learning models and data processing pipelines.
  • Conduct market research to identify trends and opportunities in AI infrastructure.
  • Work closely with engineering teams to ensure successful product delivery and integration.
  • Monitor and analyze product performance, gathering feedback to drive continuous improvement.

Requirements

  • 3+ years of experience in product engineering, specifically in AI or machine learning infrastructure.
  • Strong understanding of cloud platforms (AWS, Azure, GCP) and their services related to AI.
  • Experience with containerization technologies such as Docker and Kubernetes.
  • Excellent problem-solving skills and the ability to work in a fast-paced environment.
  • Strong communication skills to effectively collaborate with technical and non-technical stakeholders.

Nice to Have

  • Experience with data engineering and ETL processes.
  • Familiarity with AI frameworks like TensorFlow or PyTorch.
  • Knowledge of DevOps practices and CI/CD pipelines.

What We Offer

  • Competitive salary and performance-based bonuses.
  • Flexible working hours and a fully remote work environment.
  • Opportunities for professional development and training.
  • Health and wellness benefits.
  • A collaborative and innovative team culture.
Why This Job8.5 of 10

This role offers a fantastic opportunity to work remotely as an AI Infrastructure Product Engineer, with a competitive salary and the chance to innovate in AI technologies.

Salary Range
Required
0/1
Optional
0/1
Bonus
0/1

Who Will Succeed Here

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Proficient in cloud platforms such as AWS, Azure, and GCP, with hands-on experience in deploying AI models and managing infrastructure using Docker and Kubernetes.

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Demonstrates self-motivation and discipline in a remote work environment, effectively managing time and priorities while collaborating with cross-functional teams across different time zones.

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Possesses a product management mindset, with experience in gathering requirements and translating technical specifications into actionable product features that enhance AI infrastructure capabilities.

Learning Resources

→AI Infrastructure Overviewarticle
→Docker Mastery: with Kubernetes +Swarm from a Docker Captaincourse
→Cloud Computing Concepts, Part 1course

Career Path

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

Market Overview

Market Size 2024
$15B
Annual Growth
22.5%
AI Adoption
75%
Investment in AI Infrastructure
+40%
Labour Demand for AI Roles
+30%
Avg Salary for AI Infrastructure Engineers
$120K

Skills & Requirements

Required
AI InfrastructureProduct-managementAWS
Growing in Demand
Machine Learning Operations (MLOps)Serverless ComputingData Engineering
Declining
Traditional Data Center ManagementOn-Premise Virtualization

Domain Trends

Increased Cloud Adoption
As of 2024, 85% of enterprises are expected to migrate to cloud-based AI infrastructure solutions, enhancing scalability and flexibility.
Focus on AI Ethics and Governance
By 2025, 60% of AI teams will implement frameworks for AI ethics, driven by regulatory pressures and public scrutiny.
Rise of Edge AI
The edge AI market is projected to grow by 35% annually, with more companies deploying AI applications at the edge to reduce latency and bandwidth costs.

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