Principal DevSecOps & AI Platform Engineer

Infosys
Charlotte, NC, United States
about 1 month ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
7 years minimum
Compensation
$90,000.0 - $110,000.0
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Software Applications Application Performance Management Cloud Computing Cloud Computing Security Cloud Engineering Continuous Integration DevOps Python (Programming Language) Machine Learning
+17 more
Reliability Engineering Cloud Services Software Engineering TypeScript Data Logging Spring Cloud Retrieval-Augmented Generation Large Language Models Software Security Software Application Programming Reliability of Systems Generative AI Infrastructure as Code (IaC) AI Platforms Information Technology Terraform Devsecops

Job description

We are seeking a highly experienced Senior/Principal Cloud & AI Platform Engineer to design, build, secure, and operate scalable cloud platforms and AI/ML solutions. This role combines software engineering, cloud infrastructure, DevSecOps, SRE, security engineering, and AI/ML expertise., * Design, develop, and maintain scalable cloud-native applications and platforms using Python and TypeScript.

  • Architect and implement solutions within AWS, following cloud security, scalability, reliability, and performance best practices.
  • Build and maintain infrastructure using Terraform and Infrastructure as Code (IaC) principles.
  • Design, implement, and improve CI/CD pipelines to automate software delivery, testing, infrastructure deployment, and security checks.
  • Apply DevSecOps practices by integrating security throughout the software development and deployment lifecycle.
  • Partner with security teams to identify vulnerabilities, implement security controls, and improve cloud and application security.
  • Apply SRE principles to improve system reliability, availability, scalability, and operational efficiency.
  • Develop and support AI/ML solutions, including applications leveraging LLMs and Retrieval-Augmented Generation (RAG).
  • Design AI-powered applications that integrate LLMs with enterprise data, APIs, and other systems.
  • Implement observability solutions covering metrics, logs, traces, application performance, and infrastructure health.
  • Participate in incident management, including troubleshooting production issues, root cause analysis, remediation, and prevention.
  • Establish and improve monitoring, alerting, incident response, and operational procedures.
  • Collaborate with engineering, DevOps, security, data, and business stakeholders to deliver enterprise solutions.
  • Provide technical leadership, mentoring, and guidance to engineers and development teams.
  • Identify opportunities to automate manual processes and improve engineering productivity.
  • Establish engineering standards and best practices around cloud architecture, security, reliability, and AI/ML development.

Requirements

The ideal candidate will have strong hands-on experience with Python, TypeScript, AWS, Terraform, CI/CD, LLMs, and RAG architectures, along with a proven ability to improve reliability, observability, security, and operational processes. This individual will also provide technical leadership and collaborate across engineering, security, infrastructure, and product teams., * Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.

  • 7+ years of experience in software engineering, cloud engineering, DevOps, SRE, or a related technical discipline.
  • Strong hands-on experience with Python.
  • Experience developing applications or services using TypeScript.
  • Strong experience with AWS cloud services and architecture.
  • Hands-on experience with Terraform and Infrastructure as Code.
  • Experience designing and maintaining CI/CD pipelines.
  • Strong understanding of DevSecOps practices and cloud/application security.
  • Experience with SRE principles, production operations, and reliability engineering.
  • Experience with incident management, troubleshooting, and root cause analysis.
  • Strong understanding of observability, including monitoring, logging, metrics, tracing, and alerting.
  • Experience with AI/ML technologies, with practical experience working with LLMs and/or RAG architectures.

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