Principal AI Platform Architect / Principal Agentic AI Engineer

Experience: 15–20 years

Role Overview

We are looking for a Principal AI Platform Architect / Principal Agentic AI Engineer to design and build enterprise-grade AI platforms capable of supporting advanced RAG systems, autonomous and semi-autonomous agents, large-scale knowledge retrieval, LLM orchestration, and AI-enabled enterprise applications.

This individual will play a key role in defining the architecture for our next generation of AI-enabled products and internal engineering platforms.

The ideal candidate will combine deep software engineering and distributed systems experience with modern AI engineering expertise. They should understand not only how to call an LLM, but how to design reliable, observable, secure, scalable AI systems that operate against massive enterprise datasets.

We are particularly interested in candidates who have experience architecting AI platforms over terabyte- and petabyte-scale datasets and who understand RAG, agentic systems, MCP, embeddings, vector retrieval, orchestration, evaluation, and inference architecture.

Key Responsibilities

- Design enterprise AI platforms supporting RAG, agentic workflows, and intelligent automation.
- Architect AI systems capable of operating over extremely large structured and unstructured datasets.
- Design multi-agent and tool-using AI architectures.
- Design and implement Model Context Protocol-based integrations.
- Build LLM orchestration layers supporting multiple models and AI providers.
- Define patterns for routing prompts and tasks between models based on cost, latency, complexity, and capability.
- Architect enterprise Retrieval-Augmented Generation systems.
- Design document ingestion, chunking, embedding, indexing, retrieval, reranking, and context construction pipelines.




- Design hybrid retrieval strategies combining semantic, lexical, structured, metadata-based, and graph-based search.
- Develop AI memory and context-management strategies.
- Design secure tool execution frameworks for AI agents.
- Establish safeguards around permissions, data access, tool execution, and autonomous actions.
- Build AI evaluation frameworks covering retrieval quality, hallucination, faithfulness, relevance, accuracy, and task completion.
- Design observability for prompts, agents, model responses, token usage, latency, failures, and cost.
- Architect scalable inference and AI-serving platforms.
- Design AI systems capable of integrating with enterprise databases, APIs, knowledge bases, DevOps systems, support platforms, and operational systems.
- Establish AI engineering standards and reusable platform components.
- Work with product and engineering teams to convert business problems into AI-enabled workflows.
- Mentor AI engineers and software engineers.
- Evaluate emerging AI models, frameworks, orchestration platforms, and protocols.

Required Technical Expertise

- 15–20 years of experience across software engineering, distributed systems, data platforms, or AI engineering.
- Significant hands-on experience designing production-grade AI platforms.
- Deep understanding of LLMs and LLM application architecture.
- Strong expertise in RAG architectures.




- Strong understanding of embeddings and vector search.
- Experience with vector databases such as Pinecone, Milvus, Weaviate, Qdrant, pgvector, Azure AI Search, or equivalent.
- Strong experience with LLM orchestration frameworks and architectural patterns.
- Experience designing agentic AI systems.
- Robust understanding of tool calling, function calling, planning, execution, and agent state management.
- Hands-on knowledge of Model Context Protocol (MCP).
- Experience implementing secure integrations between AI agents and enterprise tools.
- Strong Python engineering skills.
- Strong software architecture and API design experience.
- Experience building large-scale distributed systems.
- Strong knowledge of cloud infrastructure and containerized workloads.
- Experience working with very large data platforms, ideally terabyte- or petabyte-scale.
- Strong understanding of structured and unstructured data retrieval.
- Experience with prompt engineering, context engineering, and AI evaluation.
- Strong knowledge of LLM security risks including prompt injection, data leakage, tool misuse, and excessive agency.

Preferred Experience

- OpenAI / Azure OpenAI.
- Anthropic Claude.
- Open-source models such as Llama, Qwen, Mistral, or equivalent.
- LangChain, LangGraph, Semantic Kernel, AutoGen, or similar frameworks.
- Kubernetes.
- GPU inference platforms.
- vLLM, Ollama, Triton, or equivalent inference technologies.
- Knowledge graphs.
- Graph-based RAG.
- Multi-model routing.
- AI gateways.
- Enterprise search.
- Petabyte-scale lakehouse or data platform architectures.
- AI coding agents and autonomous software engineering platforms.

Pay: ₹2,500,000.00 - ₹6,500,000.00 per year Work Location: Hybrid remote in Chennai, Tamil Nadu

📌 Principal AI Platform Architect / Principal Agentic AI Engineer (Chennai)
🏢 Multisolution Business Services
📍 Chennai