AI PLATFORM ENGINEER (FDE)

Vrinda International

Bengaluru

On-site

INR 2,520,000 - 3,080,000

Full time

14 days+
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Job summary

Vrinda International in Bengaluru (Bangalore) is seeking an AI Platform Engineer (FDE) to own and enhance enterprise Data & AI platforms. The role covers compute, storage, orchestration, semantic layers and AI services with a focus on automation, governance and cloud optimization.

Ideal candidates have 5–8 years of experience and shall design scalable pipelines, enable semantic models, and implement security, compliance and RBAC while driving cost efficiency.

Qualifications

  • Must-have skill focused on semantic layer and AI cost management.

Responsibilities

  • Build and operate scalable Data & AI platforms
  • Develop batch, streaming and AI-enabled pipelines
  • Enable semantic models, AI agents, MCP integrations and reusable services
  • Automate infrastructure using IaC, CI/CD and DevOps
  • Drive platform upgrades, migrations and performance optimization
  • Implement security, governance, compliance and audit controls
  • Enable secure self-service through data products and RBAC
  • Monitor platform health, SLAs and operational performance
  • Optimize cloud and AI usage costs
  • Support incident management, RCA and capacity planning
  • Collaborate with Data, AI, Analytics and business teams
  • Evaluate and operationalize emerging AI technologies

Skills

Semantic Layer & AI Cost Mgmt

Job description

Bangalore | Hybrid, India | Posted on 08/20/2026

Job Description

HIRING | AI PLATFORM ENGINEER (FDE)

Experience: 5–8 Years

CTC: Up to ₹28 LPA

ROLE OVERVIEW

We are looking for an AI Platform Engineer to own and enhance enterprise Data & AI platforms covering compute, storage, orchestration, semantic layers, AI services and platform infrastructure. The role focuses on automation, AI enablement, governance, observability and cloud optimization.

MUST-HAVE SKILLS
  • Semantic Layer & AI Cost Management
KEY RESPONSIBILITIES
  • Build and operate scalable Data & AI platforms
  • Develop batch, streaming and AI-enabled pipelines
  • Enable semantic models, AI agents, MCP integrations and reusable services
  • Automate infrastructure using IaC, CI/CD and DevOps
  • Drive platform upgrades, migrations and performance optimization
  • Implement security, governance, compliance and audit controls
  • Enable secure self-service through data products and RBAC
  • Monitor platform health, SLAs and operational performance
  • Optimize cloud and AI usage costs
  • Support incident management, RCA and capacity planning
  • Collaborate with Data, AI, Analytics and business teams
  • Evaluate and operationalize emerging AI technologies
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