📍 Job Overview

Job Title: Lead AI Systems Engineer - Dubai HQ
Company: AirNxt
Location: Dubai, United Arab Emirates
Job Type: On-site
Category: AI & Machine Learning
Date Posted: 2025-06-25
Experience Level: 5-10 years
Remote Status: On-site

🚀 Role Summary

Key Responsibilities: Design and deploy AI workflows, agent-based automation, predictive models, and NLP systems for aviation maintenance software. Collaborate with the CEO and Data Architect to define intelligent aviation software and ensure systems improve operational efficiency.

📝 Enhancement Note: This role requires a strong background in AI systems engineering, with a focus on building and shipping real-world AI/ML systems. Experience in aviation or regulated industries is a plus.

💻 Primary Responsibilities

AI Systems Design & Deployment: Lead the design and implementation of AI capabilities for aviation software, focusing on building and shipping systems that work in production.
AI Workflow Development: Develop and deploy AI workflows that turn raw maintenance documents into structured, validated data using agent-based automation and human-in-loop approval.
Predictive Modeling: Develop lightweight predictive models based on time-series or log patterns that flag potential issues before they become unscheduled events.
NLP & LLM Chaining: Implement NLP and LLM chains that extract meaning from technical and regulatory documents, pilot logs, work orders, and assist in summarizing or generating checklists.
Collaboration & Stakeholder Management: Collaborate with the CEO, Data Architect, and other stakeholders to define intelligent aviation software and ensure systems improve operational efficiency.

📝 Enhancement Note: This role requires a strong focus on execution and shipping AI systems that work in production. Experience in agile environments and solving real-world challenges is essential.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Artificial Intelligence, or a related field. A Master's degree would be an asset.

Experience: 4-8 years of experience in building and shipping real-world AI/ML systems. Experience in aviation or regulated industries is a plus.

Required Skills:

Strong Python development skills
Hands-on experience with LLM workflows, prompt engineering, and chaining frameworks (LangGraph, LangChain, Haystack, or similar)
Experience integrating LLMs, embeddings, and vector search into real-world workflows
Working knowledge of time-series or anomaly detection techniques (e.g., Prophet, LSTM, transformers)
High ownership mindset, with a focus on debugging in production and thinking in first principles
Clear communicator who thrives in lean, fast-paced teams without layers of abstraction

Preferred Skills:

Experience in aviation, logistics, or other physical/regulated systems
Familiarity with graph data (Neo4j, property graphs, entity relationships)
Experience with safety reporting systems or compliance-heavy workflows
Knowledge of smart-contracts or audit-proof data systems

📝 Enhancement Note: This role requires a strong technical background in AI systems engineering, with a focus on shipping real-world AI/ML systems. Experience in aviation or regulated industries is a plus, but not required.

📊 AI Portfolio & Project Requirements

AI Portfolio Essentials:

A portfolio showcasing your experience in building and shipping real-world AI/ML systems, with a focus on AI workflows, predictive models, and NLP systems.
Examples of your ability to design and deploy AI systems that work in production, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.

Technical Documentation:

Detailed documentation of your AI systems, including data sources, model architecture, training procedures, and evaluation metrics.
Code comments and documentation that demonstrate your attention to detail and commitment to code quality.

📝 Enhancement Note: This role requires a strong focus on shipping AI systems that work in production. A portfolio that demonstrates your ability to build and deploy real-world AI/ML systems is essential.

💵 Compensation & Benefits

Salary Range: The salary range for this role is AED 350,000 - AED 450,000 per year (USD 95,238 - USD 123,548), depending on experience and qualifications. This range is based on market research and regional adjustments for the Dubai tech industry.

Benefits:

Health insurance
The chance to own a core product stream from the start and grow into a leadership role
Early access to challenging problems with high impact in the aviation industry
A team that moves fast, thinks clearly, and backs each other
A mission that matters: keeping aircraft safer, smarter, and better maintained

Working Hours: 40 hours per week, with flexible hours to accommodate project deadlines and maintenance windows.

📝 Enhancement Note: The salary range provided is an estimate based on market research and regional adjustments for the Dubai tech industry. Actual salary offers may vary depending on the candidate's experience and qualifications.

🎯 Team & Company Context

Company Culture: AirNxt is building the next generation of AI-powered MRO software to transform aviation maintenance, operations, and management. The company values agility, innovation, and collaboration, with a focus on solving real-world aviation challenges using cutting-edge technology and innovative solutions.

Company Size: AirNxt is a growing startup, with a lean and fast-paced team that values clear communication and ownership.

Founded: AirNxt was founded in 2023, with a mission to revolutionize aviation maintenance through AI-powered software.

Team Structure:

The AI team consists of the Lead AI Systems Engineer (this role), the CEO, and the Data Architect.
The team works closely with other departments, including Engineering, Product, and Customer Success, to ensure AI systems meet the needs of users and improve operational efficiency.

Development Methodology:

AirNxt follows an agile development methodology, with a focus on building and shipping AI systems that work in production.
The team uses version control, code reviews, and automated testing to ensure code quality and maintainability.
Deployment strategies include CI/CD pipelines and server management to ensure AI systems are stable, scalable, and secure.

Company Website: AirNxt

📝 Enhancement Note: AirNxt is a growing startup with a lean and fast-paced team that values clear communication and ownership. The company culture emphasizes agility, innovation, and collaboration, with a focus on solving real-world aviation challenges using cutting-edge technology and innovative solutions.

📈 Career & Growth Analysis

AI Systems Engineer Career Level: This role is for an experienced AI systems engineer with 4-8 years of experience in building and shipping real-world AI/ML systems. The ideal candidate has a strong technical background in AI systems engineering, with a focus on shipping real-world AI/ML systems.

Reporting Structure: The Lead AI Systems Engineer reports directly to the CEO and works closely with the Data Architect to define and implement AI capabilities for aviation software.

Technical Impact: The Lead AI Systems Engineer has a significant impact on the development and deployment of AI systems that improve operational efficiency in aviation maintenance, operations, and management. Their work directly influences the user experience and the safety, reliability, and performance of aircraft.

Growth Opportunities:

Technical Leadership: As the company grows, there will be opportunities for the Lead AI Systems Engineer to take on more technical leadership responsibilities, including mentoring junior engineers and defining the technical direction of AI systems at AirNxt.
Product Ownership: With a strong focus on execution and shipping AI systems that work in production, the Lead AI Systems Engineer has the opportunity to own a core product stream from the start and grow into a leadership role.
Emerging Technology Adoption: AirNxt is at the forefront of AI-powered MRO software, and the Lead AI Systems Engineer will have early access to emerging technologies and the opportunity to shape the company's approach to AI systems engineering.

📝 Enhancement Note: This role offers significant growth opportunities for an experienced AI systems engineer looking to take on more technical leadership responsibilities and define the technical direction of AI systems at a growing startup.

🌐 Work Environment

Office Type: AirNxt's headquarters are located in Dubai, United Arab Emirates, with a modern, collaborative workspace designed to support agile development and innovation.

Office Location(s): Dubai, United Arab Emirates

Workspace Context:

Collaboration: The workspace is designed to facilitate collaboration between AI engineers, other departments, and stakeholders, with open-plan areas and dedicated meeting spaces.
Development Tools: The team uses modern development tools, including version control systems, IDEs, and cloud-based collaboration platforms, to ensure code quality and maintainability.
Testing & Deployment: The team follows best practices for automated testing, continuous integration, and deployment to ensure AI systems are stable, scalable, and secure.

Work Schedule: The work schedule is flexible, with core hours from 9:00 AM to 5:00 PM GST. The team encourages a healthy work-life balance and supports remote work when necessary.

📝 Enhancement Note: AirNxt's headquarters in Dubai offer a modern, collaborative workspace designed to support agile development and innovation. The company values clear communication and collaboration, with a focus on solving real-world aviation challenges using cutting-edge technology and innovative solutions.

📄 Application & Technical Interview Process

Interview Process:

Resume Screening: The hiring team will review your resume and portfolio to ensure your experience and skills match the requirements of the role.
Phone Screen: A brief phone call to discuss your background, experience, and motivation for the role.
Technical Deep Dive: A detailed technical interview focused on your experience in AI systems engineering, with a focus on building and shipping real-world AI/ML systems. You will be asked to discuss your approach to AI workflows, predictive modeling, and NLP systems, as well as your experience with relevant technologies and tools.
Final Interview: A conversation with the CEO to discuss your fit with the company culture, your vision for AI systems engineering at AirNxt, and your long-term goals.

Portfolio Review Tips:

Highlight your experience in building and shipping real-world AI/ML systems, with a focus on AI workflows, predictive modeling, and NLP systems.
Include examples of your ability to design and deploy AI systems that work in production, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.
Showcase your technical documentation, including data sources, model architecture, training procedures, and evaluation metrics.

Technical Challenge Preparation:

Brush up on your knowledge of AI systems engineering, with a focus on building and shipping real-world AI/ML systems.
Familiarize yourself with the latest developments in AI workflows, predictive modeling, and NLP systems.
Prepare for questions about your approach to AI systems engineering, your experience with relevant technologies and tools, and your ability to work in a lean, fast-paced team.

📝 Enhancement Note: The interview process for this role is designed to assess your technical expertise in AI systems engineering, with a focus on building and shipping real-world AI/ML systems. The hiring team will review your resume and portfolio, conduct a technical deep dive, and discuss your fit with the company culture and long-term goals.

🛠 Technology Stack & AI Infrastructure

AI Workflow & Automation Tools:

LangGraph (or equivalent) for agent-based automation workflows
Prompt engineering and LLM chaining frameworks (LangChain, Haystack, or similar)
NLP libraries and frameworks (e.g., spaCy, NLTK, Transformers)
Time-series analysis and anomaly detection libraries (e.g., Prophet, LSTM, transformers)

AI Infrastructure & Deployment:

Cloud-based infrastructure (e.g., AWS, GCP, Azure)
Containerization and orchestration tools (e.g., Docker, Kubernetes)
CI/CD pipelines and automated deployment tools (e.g., Jenkins, GitHub Actions)
Server management and configuration tools (e.g., Ansible, Terraform)

📝 Enhancement Note: The technology stack for this role is focused on AI systems engineering, with a focus on building and shipping real-world AI/ML systems. The ideal candidate will have experience with AI workflows, predictive modeling, and NLP systems, as well as relevant technologies and tools.

👥 Team Culture & Values

AI Systems Engineering Values:

Execution Focus: A strong focus on shipping AI systems that work in production, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.
Agility & Innovation: A commitment to solving real-world aviation challenges using cutting-edge technology and innovative solutions.
Collaboration & Communication: Clear communication and collaboration, with a focus on working together to define and implement AI capabilities for aviation software.
Ownership & Accountability: A high ownership mindset, with a focus on debugging in production and thinking in first principles.

Collaboration Style:

Cross-Functional Integration: Close collaboration between AI engineers, other departments, and stakeholders to ensure AI systems meet the needs of users and improve operational efficiency.
Code Review Culture: A focus on code quality and maintainability, with regular code reviews and automated testing to ensure AI systems are stable, scalable, and secure.
Knowledge Sharing & Mentoring: A commitment to sharing knowledge and mentoring junior engineers to ensure the team's technical expertise continues to grow.

📝 Enhancement Note: The team culture at AirNxt is focused on agility, innovation, and collaboration, with a strong commitment to solving real-world aviation challenges using cutting-edge technology and innovative solutions. The ideal candidate will have experience in AI systems engineering, with a focus on building and shipping real-world AI/ML systems, and a strong commitment to clear communication and collaboration.

⚡️ Challenges & Growth Opportunities

Technical Challenges:

AI Workflow Complexity: Designing and deploying AI workflows that turn raw maintenance documents into structured, validated data, with agent-based automation and human-in-loop approval.
Predictive Modeling: Developing lightweight predictive models based on time-series or log patterns that flag potential issues before they become unscheduled events, with clear traceability and fallbacks.
NLP & LLM Chaining: Implementing NLP and LLM chains that extract meaning from technical and regulatory documents, pilot logs, work orders, and assist in summarizing or generating checklists, with clear traceability and fallbacks.
AI System Integration: Integrating AI systems with existing aviation software, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.

Learning & Development Opportunities:

Emerging Technology Adoption: AirNxt is at the forefront of AI-powered MRO software, and the Lead AI Systems Engineer will have early access to emerging technologies and the opportunity to shape the company's approach to AI systems engineering.
Technical Mentoring: As the company grows, there will be opportunities for the Lead AI Systems Engineer to take on more technical leadership responsibilities, including mentoring junior engineers and defining the technical direction of AI systems at AirNxt.
Conference Attendance & Certification: AirNxt encourages its employees to attend industry conferences and pursue relevant certifications to stay up-to-date with the latest developments in AI systems engineering.

📝 Enhancement Note: This role offers significant technical challenges and growth opportunities for an experienced AI systems engineer looking to take on more technical leadership responsibilities and define the technical direction of AI systems at a growing startup.

💡 Interview Preparation

Technical Questions:

AI Systems Engineering: Can you describe your approach to AI systems engineering, with a focus on building and shipping real-world AI/ML systems? What are some of the challenges you've faced, and how have you overcome them?
AI Workflow Design: How do you design and deploy AI workflows that turn raw maintenance documents into structured, validated data? Can you walk us through an example of an AI workflow you've implemented?
Predictive Modeling: How do you approach predictive modeling, with a focus on time-series or log patterns? Can you describe a predictive model you've developed, and how you ensured its accuracy and reliability?
NLP & LLM Chaining: How do you implement NLP and LLM chains that extract meaning from technical and regulatory documents, pilot logs, work orders, and assist in summarizing or generating checklists? Can you describe a complex NLP or LLM chaining project you've worked on?

Company & Culture Questions:

Company Culture: What attracts you to AirNxt's culture, and how do you think you would fit in with our team?
AI Systems Engineering at AirNxt: What do you think are the biggest challenges and opportunities in AI systems engineering at AirNxt, and how would you approach them?
Long-Term Goals: Where do you see yourself in five years, both in terms of your career and your contributions to AI systems engineering at AirNxt?

Portfolio Presentation Strategy:

AI Systems Engineering Portfolio: Highlight your experience in building and shipping real-world AI/ML systems, with a focus on AI workflows, predictive modeling, and NLP systems. Include examples of your ability to design and deploy AI systems that work in production, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.
Technical Documentation: Showcase your technical documentation, including data sources, model architecture, training procedures, and evaluation metrics. Explain how you ensure the quality, reliability, and maintainability of your AI systems.

📝 Enhancement Note: The interview process for this role is designed to assess your technical expertise in AI systems engineering, with a focus on building and shipping real-world AI/ML systems. The hiring team will review your resume and portfolio, conduct a technical deep dive, and discuss your fit with the company culture and long-term goals.

📌 Application Steps

To apply for this Lead AI Systems Engineer - Dubai HQ position at AirNxt:

Resume Tailoring: Tailor your resume to highlight your experience in AI systems engineering, with a focus on building and shipping real-world AI/ML systems. Include relevant keywords and skills, and emphasize your ability to work in a lean, fast-paced team.
Portfolio Preparation: Prepare a portfolio that showcases your experience in AI workflows, predictive modeling, and NLP systems. Include examples of your ability to design and deploy AI systems that work in production, with clear traceability, fallbacks, and an execution path that earns trust from stakeholders and regulators.
Technical Interview Preparation: Brush up on your knowledge of AI systems engineering, with a focus on building and shipping real-world AI/ML systems. Familiarize yourself with the latest developments in AI workflows, predictive modeling, and NLP systems. Prepare for questions about your approach to AI systems engineering, your experience with relevant technologies and tools, and your ability to work in a lean, fast-paced team.
Company Research: Research AirNxt's mission, values, and culture to ensure you understand the company's focus on solving real-world aviation challenges using cutting-edge technology and innovative solutions. Consider how your experience and skills align with the company's goals and objectives.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Lead AI Systems Engineer - Dubai HQ