TekJobs - Easiest way to find your next right candidate

3 Days Ago

Offered Rate NA

Tax Type W2

Work location Grapevine-TX

Experience 8-15 Years

Key Responsibilities

Design, develop, and deploy computer vision models for real-world applications such as object detection, image segmentation, OCR, and video analytics
Build scalable, low-latency inference pipelines for real-time or near real-time systems
Develop production-quality Python code with strong engineering principles (modular design, testing, maintainability)
Deploy and manage ML models using APIs and microservices architectures
Work with cloud or edge-based environments for model deployment and optimization
Implement monitoring, model evaluation, and performance tracking (e.g., drift detection, A/B testing)
Collaborate with cross-functional teams including product managers, engineers, and data teams
Analyze data quality, perform dataset annotation/augmentation, and troubleshoot model performance issues
Optimize models for performance, accuracy, and computational efficiency (GPU/latency improvements)

Required Skills & Experience

Strong hands-on experience in Computer Vision with real-world, production-deployed systems
Proficiency in Python with solid software engineering fundamentals (OOP, APIs, testing frameworks)
Experience with CV frameworks/tools such as YOLO, OpenCV, Detectron2, PyTorch, TensorFlow
Proven experience deploying ML models into production environments (not just experimentation)
Experience with Docker, Kubernetes, CI/CD pipelines, and model deployment tools (e.g., MLflow)
Strong understanding of APIs and microservices architecture (FastAPI, Flask, etc.)
Experience working with real-time or low-latency systems
Knowledge of model monitoring, drift detection, and performance tuning
Strong understanding of the data lifecycle including annotation, augmentation, and data quality challenges
Ability to debug model performance issues and explain trade-offs in model design