AI Platform Architect

Responsibilities:

• 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, and 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.
• Mentor and grow a cross-functional team of ML engineers, platform engineers, and AI researchers.
• Design and operate multi-tenant AI platform infrastructure, ensuring isolation, fairness, and cost attribution across teams.
• Incorporate Indic language NLP requirements into platform design: multilingual embedding support, code-switching handling, and Indic model serving.

Requirements:

• Strong experience in designing and scaling AI/LLM platforms.
• Deep understanding of generative AI, LLMs, and AI system architecture.
• Hands-on experience with model training, fine-tuning, and deployment (PyTorch, LoRA, RLHF, DeepSpeed, Megatron-LM).
• Experience with inference frameworks such as vLLM, Triton, or TGI.
• Strong knowledge of RAG, Graph-RAG, vector databases, and agent orchestration.
• Understanding of distributed systems, cloud infrastructure, scalability, and security.
• Experience building production-grade, multi-tenant AI applications.
• Exposure to Indian-language NLP is preferred.
• Deep understanding of how LLMs and foundation models are trained (not just fine-tuned) model training pipelines, distributed training, and dataset construction; hands-on experience with AI safety and security model design; proficiency in AI benchmarking methodologies and multi-agent orchestration frameworks.
• Working hands-on experience with AWS (Bedrock, SageMaker), Azure AI, GCP, Docker, Kubernetes (EKS/GKE), etc. is a plus.