senior AI Engineer

About Us

At Arada, we create communities that inspire healthier, happier and more meaningful lives. Through innovative developments, exceptional customer experiences and a commitment to sustainability, we are shaping places where people can live, work and thrive.

Joining Arada means becoming part of a collaborative, ambitious and forward-thinking team dedicated to delivering excellence across every aspect of our business.

Role Purpose

Lead architecture and delivery of the organization's production AI systems — LLM applications, autonomous agents, and RAG pipelines — that power buyer-facing experiences, sales enablement, and internal operations across the group. Own technical direction for the AI Engineering practice, set eval/safety standards, and mentor the team while partnering closely with Platform, Salesforce, and ERP consultants to embed AI into core real-estate workflows.

Key Responsibilities

• Architect production-grade LLM applications, multi-agent systems, and RAG pipelines end to end
• Own model/provider selection (Anthropic Claude, OpenAI, Gemini, open-weight models) and cost/latency/quality tradeoffs
• Design context engineering strategy: chunking, retrieval, tool use, memory, and grounding against the organization's project, unit-inventory, pricing, and CRM data
• Build and govern evaluation harnesses, red-teaming, and guardrails for AI safety, hallucination control, and factual grounding on property data
• Set architecture standards, run design reviews, and mentor AI Engineers across Dubai and offshore pods
• Lead integration of AI features into Salesforce (sales/CRM), ERP (finance/procurement), and the buyer/investor portal
• Prototype and evaluate emerging AI capabilities (agentic workflows, computer-use agents, voice AI) for real-estate use cases
• Partner with Data/Platform teams on the data and vector infrastructure that AI features depend on
• Present AI roadmap, risk, and ROI to leadership; represent AI Engineering in cross-functional planning
  
  

Tech Stack

• Languages: Python (primary), TypeScript/Node.js
• LLM APIs & SDKs: Anthropic Claude (Messages API, tool use, extended thinking), OpenAI, Azure OpenAI
• Orchestration/agent frameworks: LangChain, LangGraph, LlamaIndex, Anthropic Agent SDK, Model Context Protocol (MCP)
• Vector & retrieval: Pinecone, pgvector, Weaviate, or Azure AI Search; hybrid (BM25 + embeddings) search
• Backend/API: FastAPI or Node/Express microservices, REST/GraphQL
• Cloud & infra: Azure (primary), Docker, Kubernetes/AKS, CI/CD (GitHub Actions/Azure DevOps)
• Data: PostgreSQL, Azure Blob/Data Lake, ETL pipelines (Airflow/ADF)
• Observability/Eval: LangSmith, Langfuse, custom eval harnesses, PromptFoo
• Integration targets: Salesforce APIs, Microsoft Dynamics 365 ERP APIs, document/OCR pipelines
  
  

Cutting-Edge AI Skills

• Agentic AI: designing multi-step, tool-using agents (planning, memory, self-correction) for workflows like lead qualification, unit-matching, and document review
• RAG at enterprise scale: hybrid retrieval, re-ranking, citation/grounding, freshness handling for pricing/inventory data
• Model context protocol (MCP) and tool-use design for connecting LLMs to Salesforce, ERP, and internal data
• Prompt/context engineering, structured outputs, and function/tool calling
• LLM evaluation, red-teaming, hallucination and bias mitigation, guardrail design
• Cost/latency optimization: model routing, caching, batching, prompt compression
• Familiarity with fine-tuning / LoRA and when to use it vs. prompting or RAG
  
  

Business Domain Exposure

• The organization is a large-scale master real estate developer delivering mixed-use residential, commercial, and hospitality communities across multiple master-planned developments, alongside affiliated retail, wellness/fitness, food & beverage, hospitality, education, and property-management operating brands.
• Real-estate sales & CRM AI: AI-assisted lead scoring, buyer chat assistants, unit/inventory matching for master-planned communities
• Property document intelligence: contract, SPA, and brochure summarization/extraction for sales and legal teams
• Construction & project reporting: AI-assisted dashboards drawing on ERP/project-management data
• Customer experience across retail, wellness, F&B, and hospitality operating brands within the organization's ecosystem
• Sensitivity to real-estate compliance, RERA/DLD-style regulatory context, and data confidentiality for buyer/investor information
  
  

What You'll Bring

Skill, Knowledge & Experience

Must-Have Requirements

• Strong software engineering background (Python and/or TypeScript/Node)
• Hands-on experience shipping LLM-based applications to production, not just prototypes
• Deep understanding of RAG, embeddings, vector databases, and agent frameworks
• Experience with major LLM provider APIs (Anthropic, OpenAI, or equivalent)
• Solid grasp of system design, API design, and cloud infrastructure (Azure preferred)
• Track record of technical leadership or mentoring
  
  

Nice to Have

• Experience with LLMOps tooling (evals, observability, guardrails)
• Prior real-estate, PropTech, or enterprise Salesforce/ERP integration experience
• Familiarity with fine-tuning or model customization
• Exposure to voice AI or computer-use / browser agents
  
  

Using GenAI in Your Application Process

To support a fair and consistent experience for all candidates, we ask that assessments and interviews are completed independently and without assistance from generative AI tools. This ensures we understand your strengths, your thinking, and how you approach real situations.

Why join Arada?

At Arada, you'll have the opportunity to contribute to projects that transform communities while working alongside talented professionals in a culture that values innovation, collaboration and continuous growth. We are committed to creating an inclusive workplace where everyone has the opportunity to thrive and make a meaningful impact.

Equal Opportunity Statement

Arada is committed to creating a diverse and inclusive workplace. We welcome applications from all qualified individuals and make employment decisions based on merit, experience and business needs.

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