AI Systems Architect - LLM & Vector Infrastructure

We are seeking a senior AI Systems Architect to design and implement AI-native application cores where Large Language Models (LLMs), vector databases, retrieval systems, and agent frameworks form the primary computational layer of our web and mobile applications.

This role is responsible for architecting scalable AI pipelines, retrieval-augmented generation (RAG) systems, memory architectures, AI agents, and orchestration workflows integrated with our development stack (Web, Mobile, n8n automation, and AI services).

The ideal candidate understands that AI is not a feature, it is the operating system of the product.

• AI Core Architecture Design
• Design AI-first system architecture for web and mobile applications
• Architect RAG pipelines using vector databases
• Define long-term memory, short-term memory, and contextual state systems
• Implement multi-agent AI systems
• Design AI orchestration layers
• Vector Database & Embedding Systems
• Select and implement vector databases such as:
  • Pinecone
  • Weaviate
  • Qdrant
  • Milvus
  • Supabase (pgvector)
• Optimize embedding strategies
• Implement hybrid search (semantic + keyword)
• Design scalable indexing pipelines
• LLM Integration & Optimization
• Work with models such as:
  • OpenAI APIs
  • Anthropic
  • Meta (LLaMA)
  • Deep Seek
  • Alibaba (Qwen)
• Implement structured output pipelines
• Design evaluation and prompt testing frameworks
• Optimize cost-performance ratio
• AI Agent Systems & Orchestration
• Build autonomous AI agents
• Design tool-calling systems
• Integrate with:
  • n8n
  • Lang Graph / Lang Chain style agent flows
• Implement memory-aware agents
• Production AI Engineering
• Build monitoring systems for hallucination detection
• Design guardrails and validation layers
• Implement evaluation datasets and benchmarking
• Ensure security of AI pipelines
• Build scalable infrastructure (Docker, Kubernetes, GPU optimization)

• 5+ years software engineering experience
• 2+ years building production AI systems
• Deep knowledge of:
  • Vector embeddings & similarity search
  • RAG architectures
• Tokenization and context window optimization
• Fine-tuning & LoRA concepts
• Prompt evaluation frameworks

• Designing distributed systems
• Microservices & event-driven architecture
• Experience with Postgre
  
  SQL + pgvector
• Experience deploying LLM systems in production