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Coral AI

Backend Engineer - Platform

Remote (Remote)fulltimemidAdded 1 month ago

About this role

Coral AI seeks a Backend Engineer to design and build scalable platform infrastructure for a healthcare automation startup that processes medical documents with AI. You'll establish engineering best practices, mentor peers, and help eliminate administrative bottlenecks affecting patient care timelines.

What you'll do

  • Architect and develop highly scalable, reliable backend systems and core platform infrastructure
  • Establish coding standards, conduct code reviews, and drive engineering best practices across teams
  • Identify and resolve technical debt, architectural bottlenecks, and performance issues
  • Collaborate with product, frontend, and DevOps teams to deliver integrated solutions
  • Mentor fellow engineers on platform and backend topics
  • Support company-wide product and engineering efforts through robust infrastructure

What they're looking for

  • Backend engineering (3–7 years experience)
  • Distributed systems and scalable architecture design
  • Go, Java, or Python programming
  • Cloud platforms (AWS, GCP, or Azure)
  • Docker and Kubernetes containerization
  • SQL and NoSQL databases, messaging systems, caching
  • CI/CD pipelines, testing, and code review practices
  • Problem-solving and system troubleshooting

Benefits

  • Work on cutting-edge platform engineering challenges
  • Collaborative, growth-oriented work environment
  • Competitive salary and benefits package
  • Modern office in Bengaluru
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Coral AI

Coral AI builds AI-powered healthcare automation software that processes medical documents and streamlines administrative workflows to improve patient care timelines. The company is hiring Backend Engineers and ML Engineers to develop scalable platform infrastructure and machine learning solutions for OCR, document processing, and voice technologies.

View all jobs at Coral AI

Likely interview questions

  • Describe a time you designed a scalable distributed backend system from scratch. What were the key architectural decisions, and how did you handle trade-offs between consistency, availability, and partition tolerance?
  • Walk us through your experience with cloud infrastructure (AWS/GCP/Azure) and containerization. How have you optimized costs and performance in a production environment?
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