AI Platform Engineer

As the AI Platform Architect at ConveGenius, you'll design and scale the AI infrastructure that powers model training, fine-tuning, inference, agentic workflows, and orchestration across our learning applications. You should understand how LLMs and foundation models are actually trained, not just served or fine-tuned, and have worked on AI safety and security model design. Experience with benchmarking across AI systems and hands-on work with multi-agent orchestration frameworks is preferred. This role is part of a 0-to-1 vertical build. The person joining should be comfortable with ambiguity, high ownership, rapid iteration, hands-on execution, team building, and periods of high-intensity work during the early setup phase.

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, 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, 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, or GCP; Docker, Kubernetes (EKS/GKE), etc is a plus.