Design and own end-to-end AI platform architecture: LLM serving, RAG pipelines, agentic orchestration, and model lifecycle management.

• Build scalable LLM inference infrastructure capable of serving millions of concurrent learners across diverse devices and connectivity conditions.

• Define and enforce AI safety, governance, and evaluation standards across all AI systems deployed in production.

• Drive RAG and agentic AI architectures: retrieval design, tool use, multi-agent frameworks, and orchestration patterns.

• Lead fine-tuning pipeline design: LoRA, QLoRA, RLHF, DPO from data preparation to production serving.

• Architect MLOps practices: CI/CD for models, experiment tracking, deployment versioning, and rollback mechanisms.

• Manage GPU cluster infrastructure: compute allocation, distributed training setup, and cost engineering.

• Drive AI unit-economics by setting up token-tracking, model drift metrics, and distributed tracing via OpenTelemetry, LangSmith, or Databricks.

• Collaborate with product, data, and engineering teams to translate AI platform capabilities into learner-facing features.

📌 Ai Platform Architect Noida
🏢 Smart Source
📍 Noida