Edge Video Analytics Engineer — CPU‑Only, Onsite

Cognizant; Irving (TX); On-site; USD 95,000 - 118,000; Full time

Benefits offered by this job

Medical/Dental/Vision/Life Insurance

Paid holidays plus Paid Time Off

401(k) plan and contributions

Long‑term/Short‑term Disability

Paid Parental Leave

Employee Stock Purchase Plan

Job summary

Cognizant’s IoT Practice seeks an engineer to port and optimize a containerized video analytics pipeline for CPU‑constrained routers. You’ll own model optimization, container architecture, on‑device inference, and performance tuning on Cradlepoint OS/PrplOS hardware.

Required skills include embedded systems, Docker/LXC, and CV model optimization (quantization, ONNX, TensorFlow Lite). Onsite role in Texas with strong compensation and benefits. Visa sponsorship not available.

Qualifications

• 4+ years in embedded systems or edge ML deployment.
• Experience with containerization (Docker, LXC) on constrained devices.
• ML model optimization via quantization, pruning, ONNX, TensorFlow Lite, OpenVINO.
• Video analytics / computer vision (YOLO variants, object detection pipelines).
• Python + C/C++ on Linux embedded targets.
• Cross‑compilation, profiling, and memory optimization.

Responsibilities

• Port GPU‑based video analytics models to CPU‑only router targets.
• Optimize inference pipeline to stay under 100MB memory footprint using SLMs.
• Build containerized architecture with dynamic cloud‑driven model loading.
• Tune accuracy/performance tradeoffs on ARM/MIPS router hardware.
• Integrate with Cradlepoint OS and PrplOS environments.
• Benchmark and iterate on detection accuracy vs. latency on constrained hardware.

Skills

Embedded systems

Edge ML deployment

Python

C/C++

Cross-compilation

Profiling

Memory optimization

YOLO / CV pipelines

Quantization

Pruning

ONNX

TensorFlow Lite

OpenVINO

Video analytics

Tools

Docker

LXC

Job description

Cognizant’s IoT Practice seeks an engineer to port and optimize a containerized video analytics pipeline for CPU‑constrained routers. You’ll own model optimization, container architecture, on‑device inference, and performance tuning on Cradlepoint OS/PrplOS hardware.

Required skills include embedded systems, Docker/LXC, and CV model optimization (quantization, ONNX, TensorFlow Lite). Onsite role in Texas with strong compensation and benefits. Visa sponsorship not available.