AI Engineering Manager — Vision AI & Edge Intelligence

Syanxg Technologies Pte Ltd; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Syanxg Technologies Pte Ltd in Singapore is seeking an experienced AI Engineering Manager to lead Vision AI and Edge Intelligence initiatives for real-time, low-latency applications. You will own end-to-end AI system development—perception models, multimodal reasoning, and real-time inference—driving production-grade delivery, performance, and deployment quality.

Lead a high-performing team, set technical roadmaps, and collaborate with platform, systems, and telecom partners to deliver scalable

Qualifications

• Strong background in computer vision, deep learning and production AI systems.
• Experience with edge deployment and real-time inference.
• Mastery of model optimization and deployment tooling is desirable.

Responsibilities

• Lead end-to-end Vision AI development for real-time edge deployment.
• Define and implement robust AI system architectures with cross-functional teams.
• Lead, mentor and grow a high-performing AI engineering team.

Skills

Computer vision

Deep learning

Edge AI

Production AI systems

Education

Bachelor's degree or higher in CS/AI/EE

Master's or PhD preferred for senior candidates

Tools

PyTorch

TensorFlow

ONNX

TensorRT

Job description

Role Summary

We are seeking an experienced AI Engineering Manager to lead the development of Vision AI and Edge Intelligence systems for real-time, low-latency applications.

The role focuses on building end-to-end AI systems across perception models, multimodal intelligence, real-time inference optimization, and edge deployment. This position emphasizes production-grade delivery, system performance, scalability, and real-world deployment quality.

Key Responsibilities

Vision AI Development & Edge Real-Time Inference

• Lead end-to-end Vision AI development, including object detection, segmentation, tracking, video understanding, and semantic scene understanding
• Drive the adoption of multimodal and vision-language models, including MLLMs, VLMs, and Vision Agent architectures for natural-language interaction and agentic perception workflows
• Design and optimize low-latency inference pipelines for edge deployment, balancing model accuracy, latency, compute efficiency, memory usage, and deployment feasibility
• Apply model optimization techniques such as quantization, pruning, knowledge distillation, TensorRT, ONNX, or similar production inference frameworks
• Ensure real-time system performance for production applications

Cross-Functional Integration

• Work closely with platform, system, and RAN teams to integrate AI capabilities into commercial products
• Translate product requirements into robust AI system designs and implementation plans
• Ensure AI solutions meet real-world deployment constraints, including latency, compute, reliability, and maintainability

Team Leadership

• Lead, mentor, and grow a high-performing AI engineering team
• Define the technical roadmap for Vision AI and Edge Intelligence capabilities
• Evaluate, adopt, and operationalize emerging AI technologies and system architectures

Required Skills & Experience

• Strong background in computer vision, deep learning, and production AI system development
• Proficiency in PyTorch, TensorFlow, or equivalent deep learning frameworks
• Hands-on experience with detection, segmentation, tracking, video analytics, or related vision AI applications
• Practical experience with model deployment and optimization using ONNX, TensorRT, or similar tools
• Proven ability to build and scale AI systems from prototype to production

Preferred Skills

• Experience with multimodal learning, vision-language models, foundation model adaptation, MLLMs, VLMs, or related multimodal AI systems
• Knowledge of Vision Agent concepts, including vision-language reasoning, video question answering, video summarization, visual grounding, and agentic interaction with live or recorded video streams
• Knowledge of distributed inference systems and cloud-edge collaborative architectures
• Experience with Kubernetes, containerized deployment, or cloud-edge infrastructure
• Background in real-time video processing, telecom systems, robotics, or Physical AI applications

Education & Qualifications

• Bachelor’s degree or higher in Computer Science, Artificial Intelligence, Electrical Engineering, or related technical field
• Master’s or PhD preferred for senior candidates or candidates with strong research background
• Strong foundation in machine learning, deep learning, or applied mathematics is highly desirable

Experience Requirements

• Minimum 8 years of relevant industry experience in AI / Machine Learning / Computer Vision
• Expert in Nvidia Metropolis, experience in Nvidia Isaac, Cosmos and Omniverse desired
• Proven track record of delivering production-grade AI systems in real-world environments
• Experience in edge AI, real-time systems, or large-scale deployment is highly preferred