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.

Key Responsibilities

1. 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

2. 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

3. LLM Integration & Optimization

Work with models such as:

Open AI APIs

Anthropic

Meta (LLa MA)

Deep Seek

Alibaba (Qwen)

Implement structured output pipelines

Design evaluation and prompt testing frameworks

Optimize cost-performance ratio

4. 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

5. 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)

Technical Expertise

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 & Lo RA concepts

Prompt evaluation frameworks

Experience with Python (mandatory)

Experience with Fast API / backend services

Experience designing scalable APIs

Architecture Experience

Designing distributed systems

Microservices & event-driven architecture

Experience with Postgre SQL + pgvector

Experience deploying LLM systems in production

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