We are looking for a highly experienced Senior AI Knowledge & Governance Architect to design and build the knowledge, memory, governance, and safety architecture for AI agents used across our multi-store e-commerce business.
This is not a basic Prompt Engineer, Chatbot Developer, or simple RAG role.
We need someone who can design a reliable AI knowledge system that enables AI agents to access official company information, understand which information is authoritative, identify conflicts with policies or previous decisions, respect access permissions, cite sources, and escalate issues for human review when required.
Our goal is to build a vendor-neutral AI knowledge architecture that can work across OpenAI, Claude, Gemini, and other AI providers without locking our company knowledge, rules, or historical decisions into one platform.
Key ResponsibilitiesAI Knowledge & Memory Architecture
- Design a scalable architecture for AI agent memory and company knowledge management.
- Define how company policies, workflows, project decisions, historical information, drafts, informal notes, and confidential information should be structured and managed.
- Design persistent memory and context management systems for AI agents.
- Build or recommend a reliable architecture using RAG, structured databases, vector databases, knowledge graphs, metadata, and hybrid retrieval where appropriate.
Knowledge Classification & Metadata
Design a structured knowledge model including:
- Source and source type
- Knowledge owner
- Department or team
- Scope and applicability
- Authority level
- Status
- Effective date
- Review or expiry date
- Version history
- Access permissions
- Confidentiality level
- Conflict relationships
- Retention requirements
AI Retrieval & Validation
- Design retrieval workflows that prioritize relevant, current, authorized, and reliable information.
- Develop pre-action validation checks so AI agents review relevant policies, restrictions, decisions, and permissions before performing tasks or making recommendations.
- Design mechanisms to identify outdated, duplicate, missing, or conflicting information.
Conflict Detection & Governance
Design systems that allow AI agents to:
- Detect conflicts between company policies, historical decisions, active projects, and proposed actions.
- Identify and cite the relevant sources.
- Explain conflicts clearly in plain English.
- Escalate issues to the appropriate person or team.
- Support authorized human overrides.
- Maintain an auditable record of decisions and overrides.
Security & Access Control
- Define access controls for public, internal, confidential, and restricted company knowledge.
- Recommend appropriate authentication, authorization, and permission models.
- Design processes for handling sensitive information and maintaining data privacy.
Architecture & Technology Strategy
- Evaluate and recommend appropriate technologies and architecture patterns.
- Design a vendor-neutral system that allows flexibility between different LLM providers.
- Define integration architecture for internal tools, APIs, databases, and business platforms.
- Recommend approaches for scalability, reliability, observability, security, and cost management.
Documentation & Implementation Planning
- Create architecture diagrams and technical documentation.
- Develop an implementation roadmap and phased rollout plan.
- Define testing and evaluation criteria for AI accuracy, retrieval quality, groundedness, safety, and reliability.
- Provide estimated timelines, technical requirements, and maintenance recommendations.
- Work with technical and non-technical stakeholders to explain architecture decisions clearly.
Required Skills & Experience
We are looking for someone with proven hands-on experience designing and/or implementing production-level AI, LLM, RAG, or knowledge systems.
Required Technical Experience
- 2–3+ years in GenAI/LLM/RAG/Agents + 5+ years overall technical experience in software, data, ML, or architecture.
- Strong experience with Large Language Models (LLMs) and AI applications.
- Experience designing or building AI agents or agentic workflows.
- Strong hands-on experience with Retrieval-Augmented Generation (RAG).
- Experience with persistent memory and context management for AI systems.
- Experience with structured and unstructured data.
- Experience with document ingestion and knowledge pipelines.
- Experience with metadata architecture and knowledge classification.
- Experience with vector databases and semantic search.
- Understanding of hybrid retrieval and/or knowledge graphs.
- Strong Python programming skills.
- Experience with APIs and system integrations.
- Experience with SQL and/or NoSQL databases.
AI Governance & Security Experience
Candidates should have practical experience designing or implementing some of the following:
- Source citations and traceability
- Version control and change management
- Access control and permissions
- Role-based or attribute-based access
- Human-in-the-loop approval workflows
- Manual overrides
- Conflict detection and resolution
- Audit logs and decision tracking
- Data privacy and sensitive information handling
- Knowledge retention and lifecycle management
AI Evaluation & Reliability
Experience with the following is highly preferred:
- RAG evaluation
- AI output validation
- Groundedness and faithfulness testing
- Hallucination reduction
- AI observability and monitoring
- Logging and tracing
- Performance and cost optimization
- Testing production AI systems
Preferred Technologies
Experience with some of the following technologies is preferred:
- OpenAI API
- Anthropic / Claude
- Google Gemini
- LangChain
- LangGraph
- LlamaIndex
- Vector databases
- Knowledge graphs / graph databases
- PostgreSQL or other relational databases
- MongoDB or other NoSQL databases
- Python
- FastAPI
- REST APIs
- Cloud platforms such as AWS, Google Cloud, or Azure
- Workflow automation and orchestration tools
Equivalent technologies and experience are also welcome.
Nice to Have
- Experience building AI copilots or internal AI assistants.
- Experience with multi-agent systems.
- Experience with AI governance or responsible AI frameworks.
- Experience integrating AI with Slack, Google Workspace, Shopify, CRM, helpdesk, or e-commerce platforms.
- Experience working with large volumes of company documents and operational knowledge.
- Experience designing vendor-neutral or model-agnostic AI architectures.
- Experience working with e-commerce or operational workflows.
Who Should NOT Apply
This position is not suitable for candidates whose experience is primarily limited to:
- Basic Prompt Engineering
- Simple ChatGPT usage
- Basic chatbot development
- Uploading documents into a vector database without governance
- Simple PDF question-answering applications
- No-code AI automation only
- Basic RAG projects without production architecture experience
We are specifically looking for someone who understands how to design a reliable, governed, traceable, secure, and scalable knowledge foundation for AI agents.
What We Are Looking For
The ideal candidate combines expertise in:
AI/LLM Architecture + RAG + AI Agents + Data Architecture + Knowledge Management + Governance + Security
You should be comfortable answering questions such as:
- Which company information should an AI trust?
- How should the AI determine which source has higher authority?
- What happens when two policies or decisions conflict?
- How should outdated information be handled?
- Who can access or modify sensitive information?
- When should an AI request human approval?
- How should human overrides be recorded?
- How can we audit why an AI made a recommendation?
- How can we switch AI providers without losing our knowledge and governance system?
If you have experience solving these types of problems and can demonstrate your architecture thinking through previous projects, we would like to hear from you.
Pay: ₹50,000.00 - ₹100,000.00 per month
Application Question(s):
- How many years of hands-on experience do you have designing or building AI, LLM, RAG, AI agent, or AI knowledge systems?
- Have you designed or implemented at least one production or real-world AI system involving LLMs, RAG, AI agents, or enterprise knowledge retrieval?
- Do you have hands-on experience designing or implementing Retrieval-Augmented Generation (RAG) systems?
- What is your level of hands-on experience with Python for AI, APIs, data processing, or backend development?
- Have you designed, built, or architected AI agents or agentic workflows that can use tools, APIs, workflows, or external knowledge sources?
- Do you have practical experience implementing at least some of the following: source citations, access control, versioning, audit logs, human approval workflows, or governance for AI systems?
- Briefly describe one AI/LLM, RAG, AI agent, or enterprise knowledge system that you personally designed or built. Please explain the architecture, your specific role, how knowledge was stored and retrieved, and whether the system was used in production.
- An AI agent is preparing a plan, but it finds that the proposed action conflicts with an existing company policy or a more recent decision. How would you design the system to detect the conflict, identify the correct source, explain the issue, and allow an authorized person to override the decision?
- Our company wants to use OpenAI, Claude, Gemini, or other LLM providers without losing company knowledge, historical decisions, governance rules, or workflows. How would you design a vendor-neutral architecture to achieve this?
Work Location: Remote
