Junior AI Engineer - Computer Vision at InnovationTeam

, , India -

Full Time

Start Date

Immediate

Expiry Date

16 May, 26

Salary

0.0

Posted On

15 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Vision, Deep Learning, Python, PyTorch, TensorFlow, OpenCV, Object Detection, Image Classification, Segmentation, Tracking, Docker, MLOps, GPU Optimization, APIs, Containerized Services, YOLO

Industry

Industrial Machinery Manufacturing

Description

Job Description InnovationTeam is a forward-thinking technology company that delivers advanced AI and data-driven solutions across multiple industries. We are currently seeking a Junior AI Engineer – Computer Vision to join our AI engineering team. As a Junior Computer Vision Engineer at InnovationTeam, you will be responsible for designing, developing, and deploying vision-based AI systems for real-world, production environments. You will work closely with cross-functional teams to build scalable image and video analytics solutions. This position requires strong expertise in computer vision, deep learning, and software engineering. The ideal candidate is technically strong, self-driven, and experienced in taking AI models from research to production. At InnovationTeam, we foster a culture of innovation, collaboration, and technical excellence, offering an environment where Junior engineers can make a meaningful impact. Role Overview We are seeking a skilled AI Engineer with a strong focus on Computer Vision to develop, optimize, and deploy vision-based AI solutions. The role involves working on real-world image and video analytics problems and delivering production-ready AI systems. Key Responsibilities · Develop and train Computer Vision models for image and video analysis. · Implement solutions for object detection, image classification, segmentation, and tracking. · Prepare and manage datasets, including data cleaning, labelling, and augmentation. · Optimize models for GPU performance, inference speed, and scalability. · Deploy AI models into production using APIs and containerized services. · Integrate Computer Vision models with broader AI and analytics platforms. · Collaborate with software engineers, data scientists, and product teams. · Maintain documentation and ensure code quality and reproducibility. Required Skills & Qualifications · Minimum 5 years of hands-on experience in AI / Machine Learning/Deep learning with a focus on Computer Vision. · Master’s degree in computer science, software Engineering, Artificial Intelligence, Engineering, or a related field. · Strong knowledge of Computer Vision concepts and algorithms. · Practical experience with CNN-based models and modern CV architectures. · Proficiency in Python and common ML/CV libraries (PyTorch or TensorFlow, OpenCV). · Experience with object detection and segmentation frameworks (e.g., YOLO, Detectron2). · Understanding of model evaluation, metrics, and performance optimization. · Experience deploying models in cloud or on-prem environments. · Familiarity with Docker and basic MLOps practices. · Excellent English communication skills (written and spoken). Nice to Have · Experience with video analytics or real-time inference. · Exposure to Vision Transformers or multimodal AI. · Experience with GPU-based training and inference. · Knowledge of cloud platforms (OCI, AWS, Azure, or GCP). · Background in domains such as healthcare, smart cities, or industrial AI. What We Offer · Work on practical, production-grade Computer Vision solutions. · Access to GPU infrastructure and modern AI tooling. · Collaborative, engineering-focused work environment. · Opportunities for growth and advanced AI exposure. · Competitive compensation package.

Responsibilities

The engineer will be responsible for designing, developing, and deploying vision-based AI systems for real-world production environments, focusing on building scalable image and video analytics solutions. Key tasks include developing and training Computer Vision models for analysis, implementing solutions like object detection and segmentation, and optimizing models for performance and scalability.