Software Engineer in ML

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Software Engineer in ML

i2v systems is a technology company in video surveillance and video analytics software. i2V stands for intelligent integrated video. i2V is a technology R&D company focusing on the design, development, and production of intelligent surveillance products.

The product offerings include IP video management software, intelligent video analytics/analysis using image processing, computer vision, machine, and deep learning, automatic number plate detection system, red light violation detection system, automatic traffic counting & classification (ATCC), central monitoring system, command, and control system, smart city dashboard, PSIM (physical security information management), incident management system, cloud surveillance, and mobile surveillance.

Job details

Job description:

We are looking for ML Software Engineers to join our AI team at i2v. The successful candidate will be responsible for deploying, scaling, and maintaining ML models in production environments, architect robust computer vision ML pipelines, and ensure the reliability and scalability of our ML systems.

Key Responsibilities:

Model Deployment & Integration :

Convert trained models to deployment formats (e.g., ONNX, TensorRT, OpenVINO, CoreML).
Integrate models/algorithms provided by ML engineers into applications.
Collaborate with Computer Vision ML engineers and testing/DevOps for production rollout.

Performance Optimization :

Reduce latency and memory use in VA pipeline
Optimizing and Inferencing of models
Optimizing the code for different hardware: CPU, GPU and specialized devices, on linux and Windows operating systems.

Application-Level Logic & CV Algorithm Implementation :

Creating custom business logic as per clients requirement
Implementing image processing and computer vision algorithms for diverse use cases.

Pipeline Engineering :

Building real-time or batch inference pipelines (using frameworks like Opencv or Gstreamer).

Infrastructure & Monitoring :

Track status of model inference time and memory..
Monitor model performance, latency, and hardware utilization.
Implement CI/CD for model updates.

Required Skills :

High proficiency in C, C++
Strong understanding of image processing, computer vision algorithms and machine learning
High competency with image processing, computer vision and machine learning libraries such as OpenCV, Keras, OpenVino, Deepstream, Pytorch, Tensorflow
Familiarity with Docker for containerization and deployment of applications.
Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.

Preferred skills :

Familiarity with DeepStream for real-time video analytics and processing.
Experience with OpenVINO toolkit for optimizing and deploying deep learning models on Intel hardware.
Knowledge of ONNX Runtime for inference of models trained in popular deep learning frameworks.
Experience with GStreamer for multimedia handling and streaming is a plus.

i2V Legal disclaimer: Performance metrics, deployment figures, analytics outputs, and results displayed on this website are based on specific implementation scenarios, controlled environments, or selected use cases. Actual performance may vary depending on hardware specifications, camera quality, network conditions, system configuration, site environment, lighting conditions, operational workflows, and other external factors. All images, videos, illustrations, dashboards, maps, and demonstrations shown on this website are for representational, illustrative, and informational purposes only. They are intended to showcase product capabilities and possible deployment scenarios. Unless explicitly stated, no visual content should be interpreted as depicting an actual customer site, live project, installation, location, organization, or real-world event. Any names, layouts, footage, environments, or scenarios presented may be simulated, sample-based, licensed, generic, or recreated for demonstration purposes. Similarity to any actual site, entity, or project is purely coincidental. i2V systems accepts no responsibility and shall not be held liable for any errors, omissions, misuse, assumptions, or misinterpretation of product outputs, website content, visuals, or performance claims.