1. AI Platform & LLMOps Ownership
- Define and implement AI deployment and monitoring standards.
- Build and manage CI/CD pipelines for AI systems.
- Monitor model performance, drift, hallucination, and system reliability.
- Define rollback and human-in-the-loop strategies.
2. Knowledge Base & RAG Architecture
- Design and manage scalable knowledge base architecture.
- Implement and optimize Retrieval-Augmented Generation (RAG) systems.
- Define document ingestion pipelines.
- Manage embedding strategies and retrieval logic.
- Continuously improve relevance and response quality.
3. AI Quality Engineering & Testing
- Define AI testing frameworks (functional + non-functional).
- Implement: Prompt evaluation, Output validation, Regression testing for agents, Defect tracking for AI behaviors
- Measure AI system reliability using structured KPIs.
4. Agent Lifecycle Management
- Train, fine-tune, and evaluate AI agents.
- Manage feedback loops from business users.
- Improve accuracy and reduce hallucination.
- Ensure explainability and traceability of AI decisions.
5. Responsible AI & Governance
- Ensure data privacy and compliance.
- Implement audit logs and traceability.
- Define guardrails for enterprise AI.
- Align AI governance with Bosch standards.
6. Innovation Enablement
- Identify new AI enablement tools.
- Standardize reusable AI patterns.
- Support PoCs with platform capabilities.
- Collaborate closely with Principal AI Architect.