Soul Ai
Computer Vision Engineer (Freelancer)
Skills
1 month ago
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Important information
Contract type:
Permanent
Location:
Hyderabad, Telangana, India
Work mode:
Onsite
Published on:
15 September 2026
What they need
We are looking for Indias top 1% Computer Vision Engineers for a unique job opportunity to work with the industry leaders.- Who can be a part of the community. We are looking for top-tier Computer Vision (CV) Engineers with expertise in image/video processing, object detection, and generative AI. If you have experience in this field then this is your chance to collaborate with industry leaders.
- Whats in it for you. Pay above market standards. The role is going to be contract based with project timelines from 2 12 months, or freelancing.
- Be a part of an Elite Community of professionals who can solve complex AI challenges.
- Work location could be:. Remote (Highly likely). Onsite on client location. Deccan AIs OfficeHyderabad or Bangalore. Responsibilities:. Develop and optimize computer vision models for tasks like object detection, image segmentation, and multi-object tracking.
- Lead research on novel techniques using deep learning frameworks (TensorFlow, PyTorch, JAX).
- Build efficient computer vision pipelines and optimize models for real-time performance.
- Deploy models using microservices (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
- Lead MLOps practices, including CI/CD pipelines, model versioning, and training optimizations.
- Required Skills:
- . Expert in Python, OpenCV, NumPy, and deep learning architectures (e.g., ViTs, YOLO, Mask R-CNN).
- Strong knowledge in computer vision fundamentals, including feature extraction and multi-view geometry with experience in deploying and optimizing models with TensorRT, Open VINO, and cloud/edge solutions.
- Proficient with MLOps tools (ML flow, DVC), CI/CD, and distributed training frameworks.
- Experience in 3D vision, AR/VR, or LiDAR processing is a plus.
- Nice to Have:. Experience with multi-camera vision systems, LiDAR, sensor fusion, and reinforcement learning for vision tasks.
- Exposure to generative AI models (e.g., Stable Diffusion, GANs) and large-scale image processing (Apache Spark, Dask).
- Research publications or patents in computer vision and deep learning.
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