Staff/ Principa/ MTS Agentic AI Architect – Knowledge Engineering

Vibehackers; Hyderabad; On-site; INR 3,500,000 - 7,500,000; Full time

Job summary

Vibehackers seeks a Staff/Principal AI Architect to lead enterprise architecture for Agentic AI and knowledge engineering at scale. You will drive platform patterns, governance, and cross‑functional delivery for knowledge-driven AI systems across cloud and on‑prem environments.

You will design multi‑agent architectures, knowledge fabrics, and RAG/GraphRAG solutions, guide tool access and security, and mentor engineering teams to operationalize AI‑powered decision platforms.

Qualifications

• 8+ years in software engineering, AI/ML, enterprise architecture, or knowledge engineering.
• Hands‑on or strong working knowledge of Claude ecosystem and AWS AgentCore‑like platforms.
• Experience designing agentic AI and knowledge-driven solutions across cloud and on‑prem.

Responsibilities

• Define AI strategy and enterprise architecture for Agentic AI and knowledge systems across cloud and on‑prem.
• Design scalable multi‑agent architectures and memory systems for orchestration.
• Lead RAG/GraphRAG, semantic search, grounding, and context engineering.
• Establish governance, security, observability, and Responsible AI standards across platforms.
• Collaborate with engineering, product, validation, data, and business teams; mentor engineers.

Skills

Enterprise Architecture

Knowledge Engineering

AI Strategy

Technical Leadership

System Design

Cross-functional Collaboration

Stakeholder Management

Mentoring

Governance

Security & Compliance

Observability

Problem Solving

Communication

Education

Bachelor’s degree in Computer Science / AI / Data Science / related field

Tools

Claude ecosystem

AWS AgentCore

LangChain

LlamaIndex

Neo4j / Graph DBs

Weaviate / Milvus / Qdrant

Kubernetes

BigQuery

Job description

Staff/ Principa/ MTS Agentic AI Architect – Knowledge Engineering

Explicitly calls for AI-assisted (vibe) coding and use of AI tools to automate workflows and improve efficiency.

About the Role

Lead enterprise architecture and knowledge engineering for Agentic AI at scale, designing multi-agent systems, knowledge fabrics, RAG/GraphRAG solutions, and secure hybrid cloud/on‑prem AI platforms. Drive technology selection, governance, and cross-functional delivery to operationalize AI-powered decision systems across engineering and manufacturing contexts.

Job Description

Role

Staff/Principal-level Architect for Agentic AI and Knowledge Engineering responsible for defining and driving enterprise architecture, platform patterns, and governance for agentic, knowledge-driven AI systems across cloud and on‑prem environments.

Key Responsibilities

• Define AI strategy and enterprise architecture for Agentic AI, knowledge engineering, and AI-powered decision systems across AWS, GCP, and on‑prem.
• Design scalable multi-agent architectures (A2A collaboration, memory systems, reasoning frameworks, tool use, workflow orchestration).
• Architect agentic workflows leveraging the Claude ecosystem and AWS AgentCore runtime patterns.
• Design MCP-based access patterns for secure agent connectivity to tools, APIs, knowledge repositories, and data platforms.
• Architect enterprise knowledge fabrics: ontologies, taxonomies, metadata models, and knowledge graphs for engineering and manufacturing use cases.
• Design RAG and GraphRAG solutions: retrieval, semantic search, grounding, citation, graph traversal, and context engineering.
• Develop LLM Wiki architecture, knowledge curation workflows, governance, and lifecycle processes.
• Implement semantic integration: entity resolution, schema mapping, and cross-source knowledge interoperability.
• Define hybrid platform architecture across AWS, GCP, Kubernetes, on‑prem compute, and distributed storage.
• Establish AI governance: security, compliance, access control, observability, explainability, and Responsible AI standards.
• Evaluate emerging technologies, define reference architectures, lead POCs, and drive platform adoption.
• Collaborate with engineering, manufacturing, product, validation, data, and business teams; mentor technical teams.
• Agentic AI patterns: A2A collaboration, ReAct, Plan-and-Execute, Reflection, Supervisor patterns, memory systems, tool use.
• Claude ecosystem, Claude Code-style workflows, MCP-based tool access, AWS AgentCore patterns.
• Generative AI and retrieval: LLMs, RAG, GraphRAG, semantic search, reranking, grounding, context engineering.
• Knowledge graphs and semantics: ontology engineering, taxonomy design, RDF/OWL, property graphs, Cypher, SPARQL.
• Graph databases and vector DBs: Neo4j, AWS Neptune, Pinecone, ChromaDB, Weaviate, Milvus, Qdrant, FAISS.
• AI frameworks and tooling: Python, LangChain, LlamaIndex, LangGraph, Claude Code-compatible workflows.
• Cloud and hybrid platforms: AWS, GCP, BigQuery, Kubernetes, on‑prem integrations, serverless and distributed compute.
• Enterprise content systems: JIRA, Confluence, SharePoint, Bitbucket; integration via APIs/microservices and enterprise patterns.

Qualifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related technical field.
• 8+ years experience in software engineering, AI/ML, enterprise architecture, platform engineering, or knowledge engineering.
• Hands‑on experience or strong working knowledge of the Claude ecosystem, MCP integrations, and AWS AgentCore-like platforms.
• Experience designing enterprise-scale Agentic AI, Generative AI, RAG/GraphRAG, knowledge-driven solutions across AWS, GCP, and on‑prem systems.
• Proven technical leadership, stakeholder management, cross-functional collaboration, and communication skills.
• Demonstrated experience with security, governance, observability, and operationalizing AI platforms.

Preferred Domain Exposure

• Experience applying AI and knowledge engineering in semiconductor, NAND/storage, firmware, validation, manufacturing, reliability, quality, PLM, root cause analysis, or systems engineering.

Notes

• Role expects use of AI-assisted (“vibe”) coding techniques to improve efficiency and automate workflows.

Skills

Enterprise Architecture Knowledge Engineering AI Strategy Technical Leadership System Design Cross-functional Collaboration Stakeholder Management Mentoring Governance Security & Compliance Observability Problem Solving Communication