Principal AI Architect for Enterprise AI Platform

Toptal; Ahmedabad District; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

Toptal in India is seeking a senior AI architecture leader to define the technical direction of our retrieval and knowledge layer, shaping data structures, knowledge graphs, and retrieval for LLMs and AI agents.

You will mentor engineers, own RAG/GraphRAG designs, and work with Neo4j, Weaviate, and LangChain to drive scalable, secure architectures for customer pilots.

Qualifications

• Senior- or principal-level experience in Generative AI and AI architecture.
• Strong hands-on experience designing and shipping RAG and/or GraphRAG systems in production.
• Deep understanding of knowledge graphs, ontologies, and semantic data modeling.
• Experience with graph databases (e.g., Neo4j, Neptune, TigerGraph).
• Experience with vector databases (e.g., Weaviate, Pinecone).
• Strong experience with LangChain or similar LLM orchestration frameworks.
• Track record of designing production-grade AI systems and data architectures.
• Experience mentoring engineers and explaining architectural trade-offs.

Responsibilities

• Provide senior architectural leadership for the AI platform, setting technical direction for Generative AI, retrieval, and data design.
• Design, evaluate, and improve RAG and GraphRAG architectures for enterprise AI.
• Build and optimize knowledge graph and retrieval architectures, including ontology modeling.
• Diagnose and resolve challenges in retrieval accuracy, latency, scalability, and cost.
• Improve how enterprise data is structured, indexed, and retrieved by AI agents.
• Mentor junior AI engineers through reviews, pairing, and guidance.
• Partner with leadership to keep architecture scalable as the customer base grows.

Skills

Generative AI architecture

RAG/GraphRAG design

Knowledge graphs/ontologies

LLM orchestration frameworks

Python programming

Mentoring engineers

Clear communication

Tools

Neo4j

Weaviate

LangChain

LlamaIndex

Haystack

Semantic Kernel

Job description

An enterprise AI platform is scaling quickly. It is running more customer pilots and taking on heavier platform demands, and it needs a senior architect to set its technical direction. You will own the architecture of its retrieval and knowledge layer: how enterprise data is structured, connected through knowledge graphs, and retrieved for LLMs and AI agents. You will also act as the senior technical voice for a growing engineering team. The work balances hands-on design with technical leadership. You’ll fix current problems with performance, accuracy, and scalability, and you’ll shape the approach for the customer-specific projects coming next.

What You’ll Do

• Provide senior architectural leadership across the AI platform, with clear technical direction for Generative AI, retrieval, and data design.
• Design, evaluate, and improve RAG and GraphRAG architectures for enterprise AI applications.
• Build and optimize knowledge graph and retrieval architectures, including ontology and semantic data modeling.
• Diagnose and resolve challenges in retrieval accuracy, latency, scalability, and cost.
• Improve how enterprise data is structured, indexed, and retrieved by AI agents.
• Work hands-on with Neo4j, Weaviate, and LangChain, or with equivalent graph, vector, and orchestration tools.
• Support the development of AI agents and their integration with enterprise data and workflows.
• Define technical approaches and reference architectures for upcoming customer-specific pilots.
• Mentor junior AI engineers through design reviews, pairing, and technical guidance.
• Partner with engineering leadership and technical stakeholders so the architecture stays scalable and maintainable as the customer base grows.

What You Bring

• Senior- or principal-level experience in Generative AI and AI architecture.
• Strong hands-on experience designing and shipping RAG and/or GraphRAG systems in production.
• Deep understanding of knowledge graphs, ontologies, and semantic data modeling.
• Experience with Neo4j or a comparable graph database (e.g., Amazon Neptune, TigerGraph, Stardog).
• Experience with Weaviate or a comparable vector database (e.g., Pinecone, Qdrant, Milvus, pgvector).
• Strong experience with LangChain or a comparable LLM orchestration framework (e.g., LlamaIndex, Haystack, Semantic Kernel).
• A track record of designing production‑grade AI systems, with a strong grasp of LLM‑based retrieval and data architecture.
• Experience leading or mentoring engineers within a collaborative team.
• Excellent communication skills, including the ability to explain architectural trade‑offs to technical and non‑technical stakeholders.

Nice to Have

• Experience building enterprise AI platforms or agentic AI systems.
• Background in AI governance, security, or data access controls (e.g., permission‑aware retrieval).
• Experience optimizing AI systems for accuracy, performance, and cost, including evaluation frameworks.
• Experience delivering customer‑specific AI implementations or pilots.
• Experience in a fast‑growing startup environment.
• Strong Python skills and familiarity with modern AI/ML development frameworks.