Principle AI Architect
Tasks
- Build Agentic RAG systems
- Build modular systems using SOLID principles
- Create technical specifications and architecture diagrams
- Define structured problem statements
- Design data ingestion pipelines
- Design end-to-end AI systems
- Design multi-agent systems
- Develop AI services with microservices
- Develop knowledge graphs for contextual reasoning
- Implement LLM evaluation frameworks
- Implement LLM guardrails
- Implement observability and tracing
- Implement stateful agent systems memory architectures
- Integrate AI with legacy systems
- Integrate vector databases for semantic search
- Mentor engineers and provide technical leadership
- Orchestrate AI-driven workflows
- Translate business requirements into AI architectures
Perks/Benefits
- N/A
Skills/Tech-stack
API Design | AWS | Agentic Workflows | ChromaDB | Cloud Native | Cloud-native Architectures | Data Ingestion | Data Ingestion Pipelines | Deep learning | DeepEval | Distributed Systems | Docker | Ingestion pipelines | Knowledge graphs | LLM Evaluation | Langchain | Langsmith | Language Models | Large Language Models | Machine Learning | Memory Architectures | Microservices | Milvus | Orchestration | PGVector | Prompt engineering | Python | Ragas | Retrieval-Augmented Generation | SOLID principles | Semantic Search | State management | Vector Databases
Education
N/A
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