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Early-career ML/CV specialization attracts applicants, but niche skills and non-metro hybrid role moderate competition.
Machine learning and computer vision skills are transferable, but vision-specific expertise increases domain sensitivity moderately.
Specific 1–4 years requirement plus mandatory PyTorch, CV, and production deployment skills enforce strict candidate filters.
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Design, develop, and optimize computer vision and multimodal AI systems including video analysis and vision-language models.
Implement and integrate large language models and transformer architectures for AI product features, ensuring model performance and scalability in production.
Translate AI research into practical solutions, build APIs and services, and collaborate with engineering and product teams to deliver AI-driven business solutions.
1-4 years of experience in Applied Machine Learning, Computer Vision, Deep Learning, or related fields.
Strong programming skills in Python and hands-on experience with PyTorch.
Practical knowledge of OpenCV, image/video processing, object detection/image segmentation models, and transformer architectures including Vision-Language Models.
Experience with model deployment, inference optimization, Docker, and Git.
Experience working across research-driven, fast-moving AI environments with a focus on productizing ML models.
Capability to independently investigate technical challenges and translate research into deployable AI solutions.
Strong applied ML engineering focus rather than purely experimental research, with ownership mindset for delivering scalable AI products.