# Agentic AI Systems Architect / Developer — Araby.AI

Canonical: https://jobxdubai.com/jobs/li-4439224311-agentic-ai-systems-architect-developer
Location: Abu Dhabi, UAE
Type: full_time · Level: senior
Monthly salary: AED 40,000 to 70,000 per month (estimated, not employer-stated) (UAE salaries are tax-free)
Posted: 2026-07-17
Apply: https://www.linkedin.com/jobs/view/agentic-ai-systems-architect-developer-at-araby-ai-4439224311?_l=en

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## Description

We are looking for an Agentic AI Systems Architect who can design and build production-grade AI systems beyond basic chatbots or simple RAG implementations.

The ideal candidate should understand how to architect systems where AI agents can:

• Plan and reason through multi-step tasks

• Retrieve and validate information

• Execute tool/function calls safely

• Work with memory, state, and user context

• Interact with APIs, databases, and internal systems

• Operate in secure enterprise or government environments

Basic knowledge of Node.js is required for API integration, backend connectivity, and service orchestration.

Technical Requirements

The candidate should have hands-on experience with:

Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers

Agent loops, planning/execution flows, task decomposition, reflection, validation, and human-in-the-loop workflows

Tool calling / function calling, structured outputs, JSON schemas, API execution, and tool safety controls

Stateful AI workflows, including state machines, graph-based orchestration, session state, and workflow persistence

Advanced RAG pipelines, including:

• Chunking strategies

• Embedding model selection

• Hybrid search

• Metadata filtering

• Query rewriting

• Reranking

• Context compression

• Retrieval evaluation

• Hallucination mitigation

Memory architecture, including:

• Short-term memory

• Long-term memory

• User-specific memory

• Vector memory

• Persistent knowledge stores

LLM guardrails, including:

• Prompt injection protection

• Permission-aware retrieval

• Output validation

• Policy checks

• Tool execution safety

• Response verification

LLM observability and evaluation, including:

• Tracing

• Prompt/version management

• Eval datasets

• Regression testing

• Latency analysis

• Cost monitoring

• Failure analysis

Enterprise integrations, including REST APIs, databases, CRMs/ERPs, document stores, webhooks, queues, and workflow engines

Vector databases such as Qdrant, Weaviate, Pinecone, Milvus, Chroma, pgvector, Elasticsearch, or OpenSearch

Secure deployment architectures for private cloud, on-premise, offline, or government-secure environments

Node.js basics, including API development, async workflows, service integration, and connecting AI systems to backend applications

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## Requirements

Agentic AI frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or custom orchestration layers; Agent loops, planning/execution flows, task decomposition, reflection, validation, and human-in-the-loop workflows; Tool calling / function calling, structured outputs, JSON schemas, API execution, and tool safety controls; Stateful AI workflows, including state machines, graph-based orchestration, session state, and workflow persistence; Advanced RAG pipelines, including chunking strategies, embedding model selection, hybrid search, metadata filtering, query rewriting, reranking, context compression, retrieval evaluation, hallucination mitigation; Memory architecture, including short-term memory, long-term memory, user-specific memory, vector memory, persistent knowledge stores; LLM guardrails, including prompt injection protection, permission-aware retrieval, output validation, policy checks, tool execution safety, response verification; LLM observability and evaluation, including tracing, prompt/version management, eval datasets, regression testing, latency analysis, cost monitoring, failure analysis; Enterprise integrations, including REST APIs, databases, CRMs/ERPs, document stores, webhooks, queues, and workflow engines; Vector databases such as Qdrant, Weaviate, Pinecone, Milvus, Chroma, pgvector, Elasticsearch, or OpenSearch; Secure deployment architectures for private cloud, on-premise, offline, or government-secure environments; Node.js basics, including API development, async workflows, service integration, and connecting AI systems to backend applications

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