Job at a Glance
- 📍 Location: Dubai, United Arab Emirates
- 🏢 Company: TechByte Talent Ltd
- 💼 Employment Type: Temporary
- 📊 Seniority: Mid-Senior level
- 🗂️ Function: Engineering and Information Technology
- 🏭 Industry: Technology, Information and Media and IT Services and IT Consulting
- 📅 Date Posted: 2026-08-12
- 🌐 Category: Artificial Intelligence
About the Role & Company
TechByte Talent Ltd is bringing a Artificial Intelligence Engineer on board in Dubai, United Arab Emirates. You’d be plugging into a Artificial Intelligence team where the workload is growing, not shrinking. The upside for a the right person is straightforward: growing teams promote from within more often than mature ones. Day-to-day you’d be working across the scope typical of a Artificial Intelligence Engineer — a mix of delivery and stakeholder work. Regionally, Dubai leads on both pay and visibility for artificial intelligence work; that hasn’t changed this year. Mid-career candidates can deepen technical range while picking up regional exposure. Early-career applicants get live problems from day one — which beats training-heavy graduate schemes for anyone who learns by doing.
Key Requirements
- 4+ years in software, ML, or production AI engineering, with proven experience shipping reliable services (not just prototypes)
- Deep knowledge of agentic patterns: ReAct, plan
- Comfort with experience with vector databases and retrieval systems (Pinecone, Weaviate, FAISS, Azure AI Search) and evaluation/observability tooling (LangSmith, Braintrust, Application Insights)
- Demonstrated understanding of AI security and governance: prompt injection, data exfiltration, sandboxing, and compliance
- Agent (A2A) protocols, multimodal agents, Arabic/UAE localisation experience, regulated
Job Description
We’re working with a fast-moving organisation building production-grade agentic AI systems not prototypes, not proof-of-concepts, but reasoning agents that use tools, keep up state, retrieve knowledge, execute multi-step workflows, and know when to escalate to a human.
This is a rare chance to own the full lifecycle of agentic AI in production: architecture, orchestration, tool integration, evaluation, safety, observability, and cost optimisation across Azure OpenAI, Anthropic, open models, and provider-native agent SDKs.
Design and operate production agentic AI systems with robust tool-use, memory, and multi-step reasoning
Select and integrate LLMs and agent frameworks (LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or custom orchestration), balancing latency, cost, quality and compliance
Engineer reliable tool-use patterns: function calling, structured outputs, MCP servers, permissions, retries, sandboxing, and audit trails
Build retrieval and context-engineering pipelines: RAG, hybrid search, re-ranking, short/long-term memory, and context budgeting
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this Artificial Intelligence Engineer opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, LLM, TensorFlow, PyTorch, Machine Learning, Deep Learning, NLP, Computer Vision, MLOps, Data Pipelines. ATS parsers match exact strings, not synonyms.
- Use a single-column layout. Two-column resumes break parsers — text gets scrambled or dropped.
- Save as .docx or standard .pdf (not scanned/image PDF). Avoid text boxes, headers, footers, graphics.
- Use standard section headings: Experience, Education, Skills, Certifications.
- Mirror the exact job title (Artificial Intelligence Engineer) in your target role line where truthful.
- Quantify results with numbers, percentages, currency. Metrics improve ATS scoring and human review.
- Spell acronyms out once (e.g., Search Engine Optimization (SEO)) so parser catches both forms.
- Font: Arial, Calibri, or Times New Roman at 10-12pt. Fancy fonts can render as unreadable glyphs.
Top 10 Interview Questions for This Role
- Walk me through your background and why you’re interested in this role.
- Describe a challenging project you owned end-to-end. What was the outcome?
- Tell me about a time you disagreed with a stakeholder. How did you handle it?
- How do you prioritize when everything on your plate feels urgent?
- How do you decide between fine-tuning a model vs. prompt engineering?
- Explain overfitting to a non-technical stakeholder.
- Walk through your data validation pipeline for training data.
- How do you evaluate model performance beyond accuracy?
- Describe your MLOps setup — from experimentation to production.
- How do you handle model drift once a system is in production?
How to Apply
#DubaiJobs #AIJobs #ArtificialIntelligence #Hiring #DubaiAI #JobAlert #Get9to5Jobs
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