Job at a Glance
- 📍 Location: Dubai, Dubai, United Arab Emirates
- 🏢 Company: Mindfire Technologies LLC
- 💼 Employment Type: Full-time
- 📊 Seniority: Entry level
- 🏭 Industry: Computer and Network Security
- 📅 Date Posted: 2026-08-07
- 🌐 Category: Artificial Intelligence
About the Role & Company
The AI Solutions Associate vacancy at Mindfire Technologies LLC sits in Dubai, Dubai, United Arab Emirates. The role sits inside a Artificial Intelligence team that’s actively expanding its footprint in the region. Career-wise, joining a growing team beats a static one; there’s more room to shape scope early. You’ll cover the core AI Solutions Associate remit, with room to pick up adjacent work as the team matures. Regionally, Dubai leads on both pay and visibility for artificial intelligence work; that hasn’t changed this year. Anyone at the mid-level should read this as a chance to broaden scope; anyone earlier in their career gets meaningful work sooner than most training paths would offer.
Key Requirements
- fresh graduates as well as candidates with up to 2 years of experience
- Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related discipline
- Good understanding of Artificial Intelligence, Machine Learning, Generative AI, and Large Language Models
- and familiarity with REST APIs
- Understanding of prompt engineering and LLM
- Familiarity with Microsoft Azure AI services, Azure AI Foundry, Copilot Studio, or similar AI platforms is preferred
- Basic understanding of databases, cloud platforms, APIs, and application integration
- Hands-on time with ability to understand business what’s needed and translate them into AI use cases and proof
Job Description
we’re looking for enthusiastic and technically curious candidates to join our team as an
AI Solutions Associate . This role is suitable for fresh graduates as well as candidates with up to 2 years of experience who are interested in Generative AI, AI agents, automation, and enterprise AI solutions.
Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related discipline.
Good understanding of Artificial Intelligence, Machine Learning, Generative AI, and Large Language Models.
Understanding of prompt engineering and LLM-based application development.
Exposure to AI agents, RAG, embeddings, vector databases, and knowledge-based AI applications is an advantage.
ATS Resume Tips for This Role
To get past Applicant Tracking Systems (ATS) for this AI Solutions Associate opening, tailor your resume with the pointers below:
- Include these keywords verbatim in your Skills and Experience sections: Python, Machine Learning, LLM, TensorFlow, PyTorch, 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 (AI Solutions Associate) 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
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