AI Agent Architect / Head of AI

Overview

AI Agent Architect / Head of AI Jobs in Dubai at AXX

AI Agent Architect / Head of AI

Company: Anuz Project Management Services LLC
Location: Dubai, UAE
Employment Type: Full-Time
Work Mode: On-site / Hybrid
Department: Technology / Artificial Intelligence
Reporting To: Founder / Chairman

About the Company

Anuz PMS is developing a next-generation AI Chat Agent platform designed to provide users with a highly personalized and customizable AI experience.

Our vision is to create an AI platform where every user can customize their own AI agent — including its personality, communication style, memory, knowledge, behavior, voice, tools and capabilities.

We are looking for an exceptional AI Agent Architect / Head of AI to lead the technical development of this ambitious product from architecture and MVP through production and commercial launch.

Job Summary

The AI Agent Architect / Head of AI will be the technical leader responsible for designing and developing the core intelligence of our AI platform.

The ideal candidate must have strong hands-on experience with Generative AI, Large Language Models (LLMs), AI Agents, RAG, AI memory, model orchestration, tool/function calling and scalable AI systems.

This is not a basic chatbot-development role. We are looking for a professional who can architect, build and lead the development of a production-grade AI Agent platform.

The successful candidate will work directly with the Founder/Chairman and lead the AI technology strategy, architecture and engineering team.

Key Responsibilities1. AI Agent Architecture

• Design the overall architecture of the AI Agent platform.
• Develop intelligent, stateful and customizable AI agents.
• Design single-agent and multi-agent systems.
• Build agent planning, reasoning and task-execution workflows.
• Develop reliable agent orchestration.
• Create an architecture that allows new AI capabilities to be added easily.
• Design scalable AI systems capable of supporting a growing user base.

2. LLM & AI Model Integration

• Integrate leading LLM providers and open-source AI models.
• Work with multiple AI models and providers.
• Build a model-agnostic architecture.
• Develop intelligent model routing based on task, quality, speed and cost.
• Optimize prompts, context, token usage and inference costs.
• Evaluate and implement new AI models as technology develops.
• Develop systems for maintaining consistent AI behavior.

3. AI Memory & Personalization

Design and develop a powerful AI memory system that can allow the AI to:

• Remember user preferences.
• Remember important information from conversations.
• Maintain short-term and long-term memory.
• Personalize responses for individual users.
• Understand user-specific instructions.
• Allow users to view and manage their memories.
• Allow users to edit or delete memories.
• Maintain personalized AI behavior over time.
• Separate and protect individual users’ data.

4. RAG & Knowledge Management

Design and implement advanced Retrieval-Augmented Generation (RAG) systems, including:

• Document ingestion.
• Document processing.
• Embeddings.
• Vector databases.
• Semantic search.
• Hybrid search.
• Re-ranking.
• Knowledge bases.
• User-specific knowledge.
• Company/business knowledge bases.
• AI answer evaluation.
• Retrieval accuracy optimization.

5. AI Tools & Agent Integrations

Develop a secure architecture allowing AI agents to interact with external tools and services.

Potential integrations include:

• Web search
• APIs
• Email
• Calendar
• CRM
• Business software
• File systems
• Document processing
• Payment systems
• Productivity applications
• Third-party APIs
• Future MCP/tool integrations

The architecture must allow new tools to be added without rebuilding the entire AI platform.

6. AI Customization Engine

Build the technology behind user-customizable AI agents.

Users should be able to customize:

• AI name
• AI personality
• Tone of communication
• Response length
• Communication style
• Custom instructions
• Knowledge
• Memory
• Proactive behavior
• Voice
• Avatar
• Preferences
• Rules and boundaries

The system should make advanced AI customization simple for non-technical users.

7. AI Safety & Security

Design and implement strong AI safety and security systems, including:

• AI guardrails
• Permission management
• Tool authorization
• Prompt-injection protection
• Data isolation
• User privacy
• Secure memory systems
• Content controls
• Human approval workflows where required
• Monitoring and logging
• AI evaluation
• Abuse prevention

8. Performance & Scalability

Design the platform for high performance and future commercial scale.

Responsibilities include:

• Low-latency AI responses
• Caching
• Token optimization
• AI cost optimization
• High availability
• Load management
• Distributed systems
• Monitoring
• Error handling
• Cloud scalability
• System reliability

9. Technical Leadership

• Lead the AI engineering team.
• Mentor AI engineers and developers.
• Establish AI development standards.
• Review architecture and code.
• Define technical specifications.
• Prepare the AI development roadmap.
• Evaluate technologies and vendors.
• Work closely with backend, mobile, DevOps and UI/UX teams.
• Translate business requirements into technical solutions.
• Make critical AI architecture decisions.
• Report technical progress directly to the Founder/Chairman.

Required Technical SkillsEssential Skills

• Python
• Generative AI
• Large Language Models (LLMs)
• AI Agents / Agentic AI
• RAG
• Vector databases
• Embeddings
• Prompt engineering
• Context engineering
• Function/tool calling
• AI orchestration
• API architecture
• REST APIs
• PostgreSQL
• Cloud architecture
• AI evaluation

Preferred Technologies

Experience with one or more of the following is highly desirable:

• OpenAI APIs
• Anthropic APIs
• Google Gemini
• LangGraph
• LangChain
• LlamaIndex
• Hugging Face
• PyTorch
• vLLM
• Pinecone
• Qdrant
• Weaviate
• Milvus
• Redis
• Docker
• Kubernetes
• AWS
• Microsoft Azure
• Google Cloud
• MCP
• Langfuse or similar AI observability platforms

Required ExperienceExperience Level

8+ years of total software/technology experience

with at least:

4+ years of hands-on experience in AI/ML/Generative AI

Candidates should have proven experience in:

• Production LLM applications
• AI-agent development
• AI architecture
• RAG systems
• AI memory systems
• Model integrations
• Cloud deployment
• Scalable backend systems
• AI product development
• Technical leadership

Strong Preference

We strongly prefer candidates who have personally designed and launched production AI products.

Candidates whose experience is limited to:

• Basic chatbot development
• Prompt engineering only
• Simple ChatGPT API integration
• No-code AI tools
• Academic projects without production deployment

will not be suitable for this position.

Education

Bachelor’s or Master’s degree in one of the following:

• Computer Science
• Artificial Intelligence
• Machine Learning
• Software Engineering
• Data Science
• Mathematics
• Engineering

Equivalent exceptional professional experience may also be considered.

Ideal Candidate

We are looking for someone who is:

AI Architect + Hands-on AI Engineer + Technical Leader

The ideal candidate should be able to:

Understand the vision → Design the architecture → Build the AI core → Lead the engineering team → Launch the product.

You should be comfortable working in a startup/product-development environment where requirements may evolve and where you will have significant technical ownership.

First 6-Month ObjectivesMonth 1 — Architecture

• Understand product vision and requirements.
• Finalize AI architecture.
• Select technology stack.
• Define LLM strategy.
• Define AI memory architecture.
• Define RAG architecture.
• Define security and data architecture.
• Prepare technical roadmap.

Months 2–3 — AI Core

• Build AI-agent core.
• Implement LLM orchestration.
• Implement memory.
• Implement RAG.
• Implement tool/function calling.
• Develop initial AI evaluation framework.
• Establish monitoring and logging.

Months 4–5 — Product Integration

• Integrate AI with backend and mobile application.
• Implement user personalization.
• Implement customizable AI settings.
• Optimize performance.
• Optimize AI costs.
• Conduct security and reliability testing.

Month 6 — Production MVP

• Launch production-ready AI MVP.
• Establish AI monitoring.
• Establish evaluation benchmarks.
• Improve reliability and response quality.
• Prepare architecture for large-scale commercial deployment.
• Lead the next phase of AI development.

Salary & Benefits

Salary: Competitive package, to be discussed during the interview, based on the candidate’s:

• Experience
• AI/GenAI expertise
• Production AI portfolio
• Technical leadership capability
• Architecture experience
• Previous AI product launches
• Overall suitability for the role

Performance incentives and additional benefits may be discussed with the selected candidate.

Important:

Candidates must demonstrate real hands-on experience with production AI/LLM/Agent systems.

Applications from candidates with only basic chatbot, prompt-engineering or theoretical AI experience will not be considered.

Pay: From AED5,000.00 per month

Work Location: In person

Title: AI Agent Architect / Head of AI

Company: AXX

Location: Dubai