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Machine Learning Engineer - Video Analytics
Machine Learning Engineer - Video AnalyticsEzlo Innovation • amritsar, punjab, in
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Machine Learning Engineer - Video Analytics

Machine Learning Engineer - Video Analytics

Ezlo Innovation • amritsar, punjab, in
1 day ago
Job description

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

  • Improve 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

    • Model Efficiency & Optimization

  • 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

    • Ongoing Development

  • 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

    • Required Qualifications

  • 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

    • Preferred Qualifications

  • 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)

    • 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

  • 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

    • Employment Details

  • Type : Full-time
  • Team : Cloud Infrastructure Team
  • Scope : Primary owner of CloudML video analytics platform
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