Demo

Infrastructure and Ops (Devops and MLOps) Engineer - Remote

Acunor Inc
NJ Remote Full Time
POSTED ON 2/27/2025
AVAILABLE BEFORE 5/23/2025

Primary Responsibilities :

  • Automation & DevOps : Improve existing Infrastructure as Code (IaC) according to best DevOps practices.
  • Systems Monitoring : Develop and maintain monitoring frameworks for UAIS infrastructure in relation to AI / ML training program
  • Security & Compliance : Collaborate with cybersecurity teams to ensure all systems and operations comply with industry standards and are secure against evolving threats.
  • Capacity Planning & Cost Optimization : Forecast and manage capacity requirements for the AI / ML training environment, while identifying opportunities to reduce costs without compromising performance.
  • Continuous support : Provide continuous SRE support to thousands of geographically distributed users on the UAIS platform : respond to tickets, triage support, liaise with customers.

Required Qualifications :

  • Bachelor's degree in computer science, information technology, or a related field.
  • 3 years of infrastructure experience : Proven experience working on large-scale, cloud-based, enterprise-level software platforms and deep understanding of multi-cloud architectures, specifically Azure, AWS, and GCP, with hands-on experience in cloud management.
  • 2 years of practical experience in Infrastructure-as-Code and CI / CD tools like Terraform, Git Actions and alike.
  • 2 years of practical experience in containerization technologies (Kubernetes, Docker) and orchestration
  • 2 years of practical experience in Scripting & Automation Skills : Advanced proficiency in scripting languages such as Python and Bash to support automation and system integration efforts.
  • Preferred Qualifications :

  • Security & Compliance Knowledge : Strong understanding of security best practices and experience ensuring compliance with relevant regulatory frameworks.
  • Machine Learning and LLM Operations : Exposure to modern tools and techniques in MLOps and LLMOps fields.
  • Exposure to AI / ML-specific infrastructure tools (e.g., MLflow, Kubeflow) for managing and deploying models at scale.
  • Exposure to a Regulated Industry : Experience working within a healthcare or regulated industry, with solid understanding of the unique challenges and compliance requirements.
  • Ability to work independently, manage multiple projects simultaneously, and adapt to changing priorities in a fast-paced environment.
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