AI Engineer

Key Responsibilities

Design, train, and optimize deep learning models (CNNs, SSD and related variants) for image classification and object detection.

Implement and maintain end-to-end computer vision pipelines using OpenCV or equivalent libraries.

Develop, deploy, and manage models using Azure Custom Vision and other cloud platforms.

Handle full model lifecycle : versioning, repository management, CI / CD integration, and OTA deployment.

Work closely with cross-functional teams to integrate AI models into production environments.

Monitor and evaluate model performance; retrain models to ensure accuracy and reliability.

Maintain comprehensive documentation of workflows, experiments, and deployment processes for reproducibility and compliance.

Required Skills & Qualifications

Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Machine Learning, IT , or related fields.

2–3 years of hands-on experience in computer vision, deep learning, and model deployment .

Strong programming proficiency in Python .

Experience with TensorFlow, PyTorch, and Azure Custom Vision .

Solid understanding of deep learning architectures (e.g., CNN, SSD ).

Familiarity with MLOps practices : Git, CI / CD, model versioning, OTA deployment .

Proficiency in image processing and vision techniques using OpenCV or equivalent libraries.

Experience in model evaluation, tuning, and real-world deployment.

Experience with cloud platforms such as Azure, AWS, or GCP .

Exposure to edge deployment / embedded AI systems.

Understanding of dataset management and data annotation tools.

Familiarity with containerization ( Docker ) and orchestration ( Kubernetes ) is a plus.

Soft Skills

Strong problem-solving and debugging skills.

Excellent communication and collaboration abilities.

High attention to detail and ability to work in a fast-paced environment.

Commitment to writing clean, maintainable, and scalable code.

Curiosity and eagerness to learn and explore emerging industry trends.