Video AI Engineer

Overview

On Site

$50+

Contract - W2

Skills

minimum of 3-5 years of hands?on experience developing and deploying video AI models. The ideal candidate will be an expert in computer vision modeling

video management systems (VMS)

and real?time inferencing

with proven skills in AI video preprocessing and data preparation. This role combines deep technical AI/ML expertise with practical knowledge of IT networking and video infrastructure to deliver production?ready solutions for our VaaS platform. The ideal candidate possesses a strong blend of hands-on technical experience in video solutions with strong technical acumen and strategic insight.Day-to-Day Tasks:AI Model Development & OptimizationDevelop

train

and optimize video AI models for object detection

classification

tracking

segmentation

and anomaly detectionImplement transfer learning

hyperparameter tuning

and model fine-tuning techniquesOptimize models for real-time inference using ONNX

TensorRT

OpenVINO

or similar frameworksTroubleshoot model performance

robustness

and deployment challengesVideo Data Processing & Pipeline ManagementDesign and implement large-scale video/image dataset preparation workflowsPerform data cleaning

annotation

framing

resolution normalization

and noise reductionVideo Management System (VMS) IntegrationIntegrate AI solutions with VMS platforms including Milestone

Genetec

and AvigilonWork with streaming protocols (RTSP

WebRTC

RTMP) and video codecs (H.264

H.265)Deploy real-time video analytics for surveillance and security applicationsConfigure AI models on camera systems (AXIS

Hanwha

etc.)Infrastructure & DeploymentDeploy AI models in cloud environments (AWS

GCP

Azure) and edge devicesUtilize containerization technologies (Docker

Kubernetes) and CI/CD pipelinesCollaborate with DevOps teams to ensure scalable

secure deployment architecturesImplement MLOps practices for model monitoring

retraining

and lifecycle managementSystems Integration & NetworkingConfigure enterprise network components including IP addressing

firewalls

and VPNsTroubleshoot system integrations to ensure seamless video and AI operationRequired Skill Set:Modeling & AI DevelopmentDevelop

and anomaly detection.Work with tools such as/similar to Roboflow

Ultralytics (YOLO)

CVAT

Supervisely

and TensorFlow/PyTorch pipelines.Perform transfer learning

and fine?tuning to adapt models to production environments.Optimize models for real?time inferencing using tools such as/similar to ONNX

or OpenVINO.Working knowledge and experience in PythonAI Video Preprocessing / Data PreparationExperience in preparation of video and image data for AI modeling. Example includes:Cleaning and annotating datasetsFraming

resolution adjustments

and aspect ratio normalizationNoise reduction and filteringSegmentation and labeling for supervised trainingEnsuring compatibility with AI frameworks and deployment pipelinesWork with large datasets to ensure high?quality training data for maximum model accuracy.Video & VMS IntegrationExperience with video management platforms (e.g.

Milestone

Avigilon) or integration with physical security systems.Integrate AI pipelines with Video Management Systems (VMS) for real?time analytics and monitoring. Example VMS system include Milestone

etc.Handle RTSP

RTMP

and H.264/H.265 streaming for low?latency inference.Implement real?time video AI solutions in production VaaS environments.Working knowledge of video sent over a cellular and non-cellular network and video compression formatsDeployment & InfrastructureDeploy AI models across cloud and edge environments. Experience in using Docker and Kubernetes desired but no required.Collaborate with DevOps teams for scalable and secure deployments.Leverage cloud services such as AWS/GCP/Azure services for model training

storage

and inferencing.Video over a network

Networking & IT SystemsConfigure and troubleshoot IP addressing

ports

NAT

and VPN connections.Ensure smooth VMS and AI integration within enterprise IT infrastructure.Working knowledge of network securityCamera systemsFamiliarity with camera systems configuring

networking

deploying AI models in cameras. Example camera systems include AXIS

strategic insight

and consultative client-facing experience.Design

develop

and validate architectures utilizing video solutions that support real-time video ingestion

video analytics pipelines

object or event recognition

motion detection

and metadata extraction at the edge and in the cloud. Education:Bachelor's or Master's degree in Computer Science

Electrical Engineering

Job Details

Position: Video AI Engineer
Location: Atlanta or Dallas Preferred
Duration: 12+ Months

Position Summary:

Our client, a global leader in IoT solutions is seeking a highly skilled Senior Video AI Engineer with a minimum of 3-5 years of hands?on experience developing and deploying video AI models. The ideal candidate will be an expert in computer vision modeling, video management systems (VMS), and real?time inferencing, with proven skills in AI video preprocessing and data preparation. This role combines deep technical AI/ML expertise with practical knowledge of IT networking and video infrastructure to deliver production?ready solutions for our VaaS platform. The ideal candidate possesses a strong blend of hands-on technical experience in video solutions with strong technical acumen and strategic insight.

Day-to-Day Tasks:
AI Model Development & Optimization
Develop, train, and optimize video AI models for object detection, classification, tracking, segmentation, and anomaly detection
Implement transfer learning, hyperparameter tuning, and model fine-tuning techniques
Optimize models for real-time inference using ONNX, TensorRT, OpenVINO, or similar frameworks
Troubleshoot model performance, robustness, and deployment challenges
Video Data Processing & Pipeline Management
Design and implement large-scale video/image dataset preparation workflows
Perform data cleaning, annotation, framing, resolution normalization, and noise reduction
Video Management System (VMS) Integration
Integrate AI solutions with VMS platforms including Milestone, Genetec, and Avigilon
Work with streaming protocols (RTSP, WebRTC, RTMP) and video codecs (H.264, H.265)
Deploy real-time video analytics for surveillance and security applications
Configure AI models on camera systems (AXIS, Hanwha, etc.)
Infrastructure & Deployment
Deploy AI models in cloud environments (AWS, Google Cloud Platform, Azure) and edge devices
Utilize containerization technologies (Docker, Kubernetes) and CI/CD pipelines
Collaborate with DevOps teams to ensure scalable, secure deployment architectures
Implement MLOps practices for model monitoring, retraining, and lifecycle management
Systems Integration & Networking
Configure enterprise network components including IP addressing, firewalls, and VPNs
Troubleshoot system integrations to ensure seamless video and AI operation

Required Skill Set:
Modeling & AI Development
Develop, train, and optimize video AI models for object detection, classification, tracking, segmentation, and anomaly detection.
Work with tools such as/similar to Roboflow, Ultralytics (YOLO), CVAT, Supervisely, and TensorFlow/PyTorch pipelines.
Perform transfer learning, hyperparameter tuning, and fine?tuning to adapt models to production environments.
Optimize models for real?time inferencing using tools such as/similar to ONNX, TensorRT, or OpenVINO.
Working knowledge and experience in Python
AI Video Preprocessing / Data Preparation
Experience in preparation of video and image data for AI modeling. Example includes:
Cleaning and annotating datasets
Framing, resolution adjustments, and aspect ratio normalization
Noise reduction and filtering
Segmentation and labeling for supervised training
Ensuring compatibility with AI frameworks and deployment pipelines
Work with large datasets to ensure high?quality training data for maximum model accuracy.
Video & VMS Integration
Experience with video management platforms (e.g., Milestone, Genetec, Avigilon) or integration with physical security systems.
Integrate AI pipelines with Video Management Systems (VMS) for real?time analytics and monitoring. Example VMS system include Milestone, Genetec, etc.
Handle RTSP, WebRTC, RTMP, and H.264/H.265 streaming for low?latency inference.
Implement real?time video AI solutions in production VaaS environments.
Working knowledge of video sent over a cellular and non-cellular network and video compression formats
Deployment & Infrastructure
Deploy AI models across cloud and edge environments. Experience in using Docker and Kubernetes desired but no required.
Collaborate with DevOps teams for scalable and secure deployments.
Leverage cloud services such as AWS/Google Cloud Platform/Azure services for model training, storage, and inferencing.
Video over a network, Networking & IT Systems
Configure and troubleshoot IP addressing, ports, firewalls, NAT, and VPN connections.
Ensure smooth VMS and AI integration within enterprise IT infrastructure.
Working knowledge of network security
Camera systems
Familiarity with camera systems configuring, networking, deploying AI models in cameras. Example camera systems include AXIS, Hanwha etc.
Architect and customer-facing skills
Evaluate and recommend video analytic solutions to propose to customers based on requirements gathering.
The ideal candidate possesses a strong blend of hands-on technical experience in video solutions with strong technical acumen, strategic insight, and consultative client-facing experience.
Design, develop, and validate architectures utilizing video solutions that support real-time video ingestion, video analytics pipelines, object or event recognition, motion detection, and metadata extraction at the edge and in the cloud. Education:
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Job Details

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