Job description
Our client is a frontier AI and applied research lab building real-world intelligence systems for mobility, logistics, transport, and physical operations.
Combining AI, machine learning, computer vision, GIS, data science, operations research, and applied mathematics, the team develops advanced solutions that help organizations understand, optimize, and automate how people, goods, vehicles, and infrastructure move.
From intelligent video analytics and edge AI to routing optimization, operational automation, and data-driven decision systems, our client works at the intersection of deep technology and real-world execution - turning complex operational challenges into scalable, production-ready AI products.
About the Role We are looking for a highly capable Machine Learning Engineer / Applied AI Engineer with a strong multidisciplinary ML background and the ability to solve complex, real-world problems across multiple AI domains.
This role is ideal for someone who is not limited to one narrow specialization, but can think across computer vision, NLP/LLMs, MLOps, audio AI, edge deployment, and mathematical model design .
You will work on applied AI systems that need to run reliably in real operational environments, including on-premises, edge devices, hybrid infrastructure, and cloud platforms .
The ideal candidate should combine strong engineering capability with mathematical depth, practical deployment experience, and the ability to evaluate models rigorously beyond surface-level experimentation.
WHAT WE OFFER: Competitive salary benchmarked to market Direct exposure to cutting-edge enterprise AI projects across UAE and GCC Flat team structure - work directly with founders and senior engineers Opportunity to grow into a Senior or Lead Engineer role Flexible remote/hybrid work arrangements Fast-paced environment where your work ships to production and reaches real clients Key Responsibilities Design, build, evaluate, and deploy machine learning models for real-world AI applications.
Work across multiple ML domains such as computer vision, NLP/LLMs, MLOps, and audio AI .
Develop AI solutions that can operate in on-premises, edge, hybrid, and cloud environments .
Translate ambiguous business or operational problems into structured ML approaches.
Evaluate problems from different machine learning perspectives and select the most suitable technical path.
Apply strong mathematical reasoning to model selection, model design, validation, and performance evaluation.
Build scalable ML pipelines, inference workflows, and deployment-ready AI systems.
Work closely with engineering, product, and research teams to turn prototypes into reliable production systems.
Support deployment standards for environments where cloud-native assumptions may not apply.
Ensure models are tested, monitored, optimized, and production-ready for real-world use cases.
Required Skills and Experience Strong multidisciplinary machine learning background across at least two of the following areas: Computer Vision NLP / Large Language Models MLOps Audio AI / Speech AI Strong mathematical foundations in areas such as: Linear algebra Probability and statistics Optimization Numerical methods Model evaluation and validation Hands-on experience deploying AI/ML systems in on-premises and edge device environments .
Familiarity with cloud infrastructure, particularly one or more of: AWS, Microsoft Azure, Microsoft technology stack Practical experience taking ML models from experimentation to deployment.
Ability to work across different company or project contexts, rather than experience limited to one employer or one broad job title.
Strong understanding of model performance, trade-offs, latency, scalability, and infrastructure constraints.
Experience with production-grade ML engineering, not just research notebooks or proof-of-concepts.
What Good Looks Like; The successful candidate should demonstrate: Cross-domain problem solving - able to approach a problem through multiple ML lenses, not just one preferred technique.
Mathematical rigour - able to explain why a model, metric, architecture, or evaluation method is appropriate.
Deployment maturity - understands the difference between cloud-native deployment and real-world on-premises or edge deployment.
Practical AI mindset - focused on building systems that work reliably in production, not just experimental demos.
Adaptability - comfortable working across different industries, technical environments, and operational constraints.
Nice to Have: Experience with real-time inference systems.
Experience with video analytics, object detection, tracking, or sensor-based AI.
Experience with LLM-powered workflows, RAG, agents, or enterprise AI systems.
Experience with containerization, orchestration, and deployment automation.
Familiarity with NVIDIA edge/cloud AI tooling, GPU optimization, or inference acceleration.
Experience working in startup, scale-up, research lab, consulting, or product engineering environments.
Ideal Candidate Profile This role would suit someone who has worked across multiple AI/ML domains and enjoys solving hard, ambiguous, applied problems.
You may have come from a research-heavy engineering background, an applied AI product team, an advanced analytics environment, or a technical consulting/product delivery setting.
What matters most is your ability to combine mathematical depth, ML breadth, engineering execution, and real-world deployment thinking .
This job post has been translated by AI and may contain minor differences or errors.
