Role: AI Vision Lead— Edge & Multimodal AI
Business Unit:
AI & Cybersecurity Practice
Location:
(Onsite) WFO
Role Overview
We are seeking a
hands-on AI Vision Lead
to drive the design, development, and deployment of computer vision capabilities for industrial safety and operational intelligence solutions.
This role combines
technical leadership with strong execution ownership
, covering the full lifecycle of AI vision systems — from data strategy and model development to real-time deployment on edge platforms and continuous improvement in live production environments.
Key Responsibilities
Lead development of computer vision solutions for industrial monitoring, safety intelligence, and inspection automation
Define dataset strategy, annotation workflows, validation approaches, and performance benchmarks
Architect and optimize deployment of real-time AI models on edge hardware (e.g., NVIDIA Jetson or similar platforms)
Ensure reliability, scalability, and latency optimization for multi-camera video analytics systems
Integrate perception outputs with enterprise applications, alert engines, and operational workflows
Monitor and improve model performance in production through systematic error analysis, dataset refinement, and retraining cycles
Evaluate and gradually adopt
modern multimodal AI approaches
such as video event understanding, vision-language reasoning, synthetic data usage, and sensor fusion
Mentor engineers and collaborate with customers and stakeholders to translate operational challenges into scalable AI solutions
Required Skills & Experience
3 to 5 years of experience in computer vision or applied AI system development in production environments
Strong hands-on expertise in Python and deep learning frameworks (PyTorch / TensorFlow)
Experience building object detection or video analytics solutions
Exposure to deploying optimized models for real-time inference (ONNX / TensorRT / Edge AI preferred)
Experience managing training data workflows including annotation coordination, label quality validation, and dataset versioning
Familiarity with Linux environments, Docker, and video processing pipelines
Strong understanding of model evaluation metrics, reliability considerations, and real-world validation
Preferred
Experience with edge AI platforms and multi-stream video inference optimization
Awareness or exposure to
multimodal AI concepts
such as vision-language models or temporal video intelligence
Experience in industrial, manufacturing, or safety-critical AI deployments
Experience handling challenging visual conditions such as fire/smoke detection and false-positive reduction in complex environments
Familiarity with model lifecycle management or MLOps practices
What Success Looks Like
Robust AI vision capabilities deployed reliably across industrial environments
Continuous improvement in detection accuracy, system performance, and scalability
Clear evolution path toward next-generation multimodal AI vision systems
