Machine Learning Engineer - Video AnalyticsEzlo Innovation • amritsar, punjab, in
Machine Learning Engineer - Video Analytics
Ezlo Innovation • amritsar, punjab, in
1 day ago
Job descriptionImprove detection accuracy across all object classes (person, vehicle, animal, package, face, license plate) Reduce false positive and false negative rates Fine-tune and retrain YOLO models on domain-specific datasets Evaluate and integrate newer model architectures as they become available Develop robust evaluation metrics and benchmarking pipelines Optimize inference speed and resource utilization Implement model quantization, pruning, or distillation techniques Balance accuracy vs. latency tradeoffs for real-time processing Optimize frame skip strategies and batch processing Profile and eliminate performance bottlenecks in the inference pipeline Maintain and extend the existing detection pipeline Add support for new object classes as business needs evolve Improve hotzone (region-of-interest) detection logic Enhance face recognition matching accuracy Collaborate with the cloud team on infrastructure and scaling Strong experience with object detection models , particularly YOLO family (v5 / v8 / v11), and understanding of model training, fine-tuning, and evaluation Hands-on experience with PyTorch for model development and optimization Computer vision fundamentals : image / video processing, frame extraction, bounding box handling, NMS, confidence thresholds Python proficiency with production-quality code practices Experience deploying ML models in production environments (containerization, GPU inference, batching strategies) Understanding of model optimization techniques : quantization, TensorRT, ONNX conversion, etc. Familiarity with Linux / Docker / Kubernetes environments Experience with video analytics or surveillance systems Background in face recognition systems Experience with license plate recognition (ALPR / OCR) Familiarity with zero-shot detection models (YOLOWorld, CLIP-based approaches) Experience with dataset curation, annotation, and augmentation Knowledge of edge deployment considerations (model size, latency constraints) Experience with distributed job queues (Redis Queue, Celery) Measurable improvements in detection accuracy (precision / recall) across object classes Reduced inference time per video while maintaining or improving accuracy Well-documented model evaluation pipelines and benchmarks Proactive identification and resolution of model performance issues Clear communication with the broader team on ML capabilities and limitations Type : Full-time Team : Cloud Infrastructure Team Scope : Primary owner of CloudML video analytics platform
About Ezlo Innovation
Ezlo Innovation is a leading IoT platform developer powering smart home and property management solutions across 60+ countries. Our family of brands—including Vera, MiOS, Fortrezz, and Centralite—brings nearly 50 years of combined experience in home automation and IoT markets. We deliver cloud-to-ground, white-label IoT solutions to security dealers, property management companies, builders, utilities, and retail partners worldwide.
About the Role
We're looking for a Machine Learning Engineer to own and advance our CloudML video analytics platform. This system processes video from smart home cameras to detect objects including people, vehicles, animals, packages, faces, and license plates. You'll be the primary developer responsible for this project while collaborating with our broader cloud infrastructure team.
This role focuses heavily on ML model performance —improving detection accuracy, reducing false positives / negatives, and optimizing model efficiency for real-time video processing at scale.
The Platform
CloudML v2 is a production video object detection service built on :
- ML / Computer Vision : YOLO v8 / v11, YOLOWorld (zero-shot detection), PyTorch, OpenCV, face-recognition, OpenALPR
- Backend : Python 3.11, FastAPI, Redis Queue (RQ)
- Infrastructure : Kubernetes, Helm, Docker, NVIDIA GPUs (CUDA 12.6)
- Observability : OpenTelemetry, structured logging
The system processes MP4 video files through a multi-stage pipeline : download, validation, frame extraction, multi-model inference (standard objects, packages, faces, license plates, barcodes), and webhook delivery of results.
Primary Responsibilities
Model Performance & Accuracy
Model Efficiency & Optimization
Ongoing Development
Required Qualifications
Preferred Qualifications
Tech Stack You'll Work With
Category
Technologies
ML Frameworks
PyTorch, Ultralytics YOLO, YOLOWorld, OpenCV, Supervision
Specialized ML
face-recognition (dlib), OpenALPR, Tesseract OCR, pyzbar
Backend
Python 3.11, FastAPI, Redis, RQ (Redis Queue)
Infrastructure
Kubernetes, Helm, Docker, GitLab CI / CD
Hardware
NVIDIA GPUs, CUDA 12.6
Observability
OpenTelemetry, structured logging
What Success Looks Like
Employment Details
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Machine Learning Engineer • amritsar, punjab, in
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