AI Lead Engineer

Careernet; Ajman; On-site; AED 367,000 - 661,000; Full time

Job summary

Careernet in Ajman, UAE, is seeking an AI Lead Engineer to architect and lead the intelligent software layer for an AI and robotics system. You will drive integration of AI models with perception, decision-making, and robotic execution in edge/on-premise environments.

The role requires hands-on AI engineering, experience deploying AI models in production, and expertise across Computer Vision, ML, GPU computing, and performance optimization.

Qualifications

• Hands-on AI/ML engineering and architecture experience.
• Experience deploying AI/ML models in production.
• Strong knowledge of Computer Vision, Deep Learning, and AI inference.
• Experience with AI orchestration, workflows, and distributed systems.
• Proficiency with Python; C++ preferred.

Responsibilities

• Lead the design and development of the AI intelligence and orchestration layer connecting perception, AI inference, decision-making, and robotic execution.
• Develop reliable AI workflows, state machines, and system logic for deterministic robotics operations.
• Integrate Computer Vision, ML/Deep Learning, and AI models into edge/on-premise robotics environments.
• Design APIs, microservices, and interfaces connecting AI, perception, robotics, and execution systems.
• Optimize GPU/compute performance, inference latency, throughput, and resource utilization for real-time workloads.
• Own production reliability including deployment, monitoring, logging, security, and continuous performance optimization.

Skills

AI/ML Engineering

Python

C++

AI Architecture

Computer Vision

Robotics AI

Deep Learning

GPU Computing

Performance Engineering

Security

Tools

REST APIs

gRPC

Docker

Linux

Job description

About the job:

Position: AI Lead Engineer – AI & Robotics

Location: Ajman, UAE

We are looking for an AI Lead Engineer to lead the development of the intelligent software layer powering an advanced AI and robotics/automation system.

The role sits at the intersection of Artificial Intelligence, Computer Vision, Robotics, Edge Computing, AI Infrastructure, and Systems Engineering. You will be responsible for building the central intelligence and orchestration layer that connects AI models and perception systems with decision-making and real-world execution.

The ideal candidate is a hands-on AI engineer/architect who has experience taking AI systems from model development and integration through production deployment, particularly in edge, on-premise, or hardware-connected environments.

Key Responsibilities:

• Lead the design and development of the AI intelligence and orchestration layer connecting perception, AI inference, decision-making, and robotic execution.
• Develop reliable AI workflows, state machines, and system logic for deterministic and recoverable robotics operations.
• Integrate Computer Vision, ML/Deep Learning, and AI models into edge/on-premise robotics and automation environments.
• Design and develop APIs, microservices, and communication interfaces connecting AI, perception, robotics, and execution systems.
• Optimize GPU/compute performance, inference latency, throughput, and resource utilization for real-time or near-real-time AI workloads.
• Own production reliability, including deployment, monitoring, logging, security, troubleshooting, and continuous performance optimization.

Qualifications & Skills:

• Strong hands-on experience in AI/ML Engineering, AI Architecture, Computer Vision, or Robotics AI, with strong Python skills; C++ is preferred.
• Experience deploying AI/ML models in production, particularly in edge, on-premise, or hardware-connected environments.
• Strong understanding of Computer Vision, Deep Learning, AI inference, GPU computing, and model deployment/lifecycle management.
• Experience with AI orchestration, workflow engines, state machines, distributed systems, or robotics system coordination.
• Experience with REST APIs, gRPC, message queues, microservices, Docker, and Linux-based environments.
• Experience with AI/ML frameworks such as PyTorch, TensorFlow, OpenCV, Hugging Face, or similar technologies.
• Strong understanding of performance engineering, including latency, throughput, profiling, GPU utilization, and compute resource management.
• Good understanding of AI/IT security, including IAM/RBAC, secrets management, secure networking, firewalling, and data governance.
• Experience with robotics, automation, autonomous systems, cyber-physical systems, sensors, actuators, or robotics middleware such as ROS/ROS2 is highly desirable.
• Exposure to edge AI, real-time systems, LLMs/VLMs, on-premise AI models, GPU inference technologies such as CUDA/TensorRT/vLLM, and cloud/CI-CD infrastructure would be an advantage.