About the Role
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The company is looking for an AI Product Engineer (Computer Vision & VLM) to develop, integrate, optimize, and deploy Vision Language Models (VLMs), computer vision solutions, and real-time video analytics capabilities across its Baseer product portfolio. The role focuses on transforming live camera feeds, recorded videos, and images into actionable insights, structured events, and AI-driven workflows for enterprise applications.
Key Responsibilities
Computer Vision Development
- Develop and integrate computer vision models for object detection, tracking, classification, segmentation, and visual event recognition.
- Implement solutions using YOLO, OpenCV, PyTorch, and other modern computer vision frameworks.
- Design video analytics pipelines for multi-camera environments.
- Build use cases involving people counting, occupancy, object tracking, activity recognition, safety compliance, and operational monitoring.
- Implement video analytics workflows involving regions of interest, event triggers, and configurable business rules.
- Evaluate accuracy under real-world conditions, including variable lighting, occlusions, camera angles, and environmental changes.
Vision Language Models & Multimodal AI
- Integrate VLMs to analyze images, video frames, and visual events using natural-language instructions.
- Develop visual question answering, image understanding, scene description, and visual reasoning capabilities.
- Combine traditional computer vision models with VLMs to improve contextual understanding and event interpretation.
- Build video summarization, semantic video search, and natural-language querying capabilities.
- Develop multimodal workflows combining visual intelligence with LLMs, RAG, and AI agents.
- Evaluate VLM performance, accuracy, hallucinations, latency, and inference cost.
Real-Time Video Processing
- Design and implement video ingestion and processing pipelines using RTSP, GStreamer, FFmpeg, or equivalent technologies.
- Build scalable architectures for processing multiple concurrent video streams.
- Implement frame sampling, batching, inference scheduling, stream recovery, and efficient resource utilization.
- Optimize video processing performance to meet target FPS, latency, and infrastructure requirements.
- Transform video events into structured metadata, alerts, searchable information, and business insights.
- Work with both live camera feeds and recorded video sources.
AI Model Deployment & Optimization
- Deploy and optimize computer vision and VLM models on cloud, on-premises, and edge infrastructure.
- Work with NVIDIA GPUs and edge AI platforms such as NVIDIA Jetson.
- Support inference optimization using TensorRT, ONNX Runtime, or equivalent frameworks.
- Explore deployment and optimization on Qualcomm Cloud AI accelerators where supported.
- Implement model benchmarking, performance monitoring, and resource utilization tracking.
- Integrate models with video management systems and enterprise applications.
Product Engineering
- Develop backend APIs and services to integrate AI models into web-based products and dashboards.
- Build reusable computer vision pipelines and configurable analytics components.
- Collaborate with product managers and UI/UX engineers to deliver complete product features.
- Create configurable use cases rather than hardcoded customer-specific implementations.
- Contribute to product architecture, technical documentation, automated testing, and release processes.
- Own assigned features from development through deployment and ongoing optimization.
Required Technical Skills/Experience
- 3–6 years of relevant experience.
- Strong Python programming experience.
- Hands-on experience with OpenCV, PyTorch, and computer vision models.
- Practical experience with object detection and tracking models, including YOLO.
- Experience processing images and video from cameras or recorded sources.
- Strong understanding of image preprocessing, inference pipelines, and model performance metrics.
- Familiarity with VLMs such as Qwen-VL, LLaVA, or equivalent.
- Experience developing backend APIs using FastAPI or similar frameworks.
- Familiarity with Linux, Docker, Git, and AI deployment workflows.
- Ability to troubleshoot model accuracy, performance, and video pipeline issues.
- Fluency in both Arabic and English, with strong verbal and written communication skills.
Preferred Qualifications
- Experience with real-time multi-camera video analytics platforms.
- Knowledge of RTSP, GStreamer, FFmpeg, and video codecs such as H.264/H.265.
- Experience with tracking frameworks such as ByteTrack or DeepSORT.
- Experience with segmentation, pose estimation, OCR, and action recognition.
- Familiarity with video-language models and temporal video understanding.
- Experience with ONNX, TensorRT, model quantization, and inference acceleration.
- Experience deploying AI workloads on NVIDIA Jetson, NVIDIA GPUs, or Qualcomm AI platforms.
- Knowledge of video management systems, camera integration, and edge computing.
- Experience with Kubernetes and distributed inference architectures.
- Experience developing AI solutions for transportation, aviation, surveillance, industrial inspection, or smart cities.
