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Ai/Llm Platform Engineer

India, Bangalore · Job Posted May 27, 2026
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Job Description

We are looking for a highly skilled AI/LLM Platform Engineer with expertise in Python, LLM APIs, and enterprise AI platform development. The role involves designing, building, and scaling LLM-powered services with strong focus on observability, governance, and performance. The ideal candidate will have hands-on experience with AI gateways, agent frameworks, and cloud AI platforms such as Azure and GCP Vertex AI.

Job Responsibility

  • Design and develop LLM APIs (chat, generate, search, ingest)
  • Manage end-to-end API lifecycle including deployment and monitoring
  • Implement and operate AI Gateway capabilities (streaming, rate limits, quotas)
  • Build telemetry and observability frameworks using OpenTelemetry and tracing
  • Develop SDKs using LangGraph / ADK frameworks
  • Create and manage agent-based workflows
  • Implement batch and async API patterns with promotion gates
  • Ensure AI safety and governance through guardrails and content safety tools
  • Collaborate with cross-functional teams to integrate AI solutions at scale
  • Optimize performance, reliability, and scalability of AI platforms

Requirements

  • Strong programming skills in Python
  • Hands-on experience in LLM API design and implementation
  • Experience with API lifecycle management
  • Knowledge of AI Gateway operations (streaming, throttling, quotas)
  • Experience in Telemetry & Observability (OpenTelemetry, trace propagation)
  • Hands-on experience with SDK development (LangGraph / ADK)
  • Strong understanding of distributed systems and microservices architecture

Nice to have

  • Experience with Agent Frameworks (LangGraph / ADK)
  • Knowledge of LLM observability (spaces/projects)
  • Familiarity with batch & async API patterns
  • Experience with AI safety tools (Model Armor, Azure Content Safety, Guardrails)
  • Exposure to AI coding assistants (GitHub Copilot, Devin, Cursor)
  • Hands-on experience with GCP Vertex AI and Microsoft Azure environments
  • Understanding of LLMOps / MLOps practices

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