Job
Description
Role Overview: - Build and own AI-backed features end to end, from ideation to production including layout logic, smart cropping, visual enhancement, out-painting, and GenAI workflows for background fills. - Design scalable APIs that wrap vision models like BiRefNet, YOLOv8, Grounding DINO, SAM, CLIP, ControlNet, etc., into batch and real-time pipelines. - Write production-grade Python code to manipulate and transform image data using NumPy, OpenCV (cv2), PIL, and PyTorch. - Handle pixel-level transformations from custom masks and color space conversions to geometric warps and contour ops with speed and precision. - Integrate models into the production web app (AWS-based Python/Java backend) and optimize them for latency, memory, and throughput. - Frame problems when specs are vague - help define what good looks like and then build it. - Collaborate with product, UX, and other engineers without relying on formal handoffs - you own your domain. Key Responsibilities: - 23 years of hands-on experience with vision and image generation models such as YOLO, Grounding DINO, SAM, CLIP, Stable Diffusion, VITON, or TryOnGAN including experience with inpainting and outpainting workflows using Stable Diffusion pipelines. - Strong hands-on knowledge of NumPy, OpenCV, PIL, PyTorch, and image visualization/debugging techniques. - 12 years of experience working with popular LLM APIs such as OpenAI, Anthropic, Gemini, and how to compose multi-modal pipelines. - Solid grasp of production model integration - model loading, GPU/CPU optimization, async inference, caching, and batch processing. - Experience solving real-world visual problems like object detection, segmentation, composition, or enhancement. - Ability to debug and diagnose visual output errors - e.g., weird segmentation artifacts, off-center crops, broken masks. - Deep understanding of image processing in Python: array slicing, color formats, augmentation, geometric transforms, contour detection, etc. - Experience building and deploying FastAPI services and containerizing them with Docker for AWS-based infra. - A customer-centric approach - thinking about how your work affects end users and product experience, not just model performance. - A quest for high-quality deliverables - writing clean, tested code and debugging edge cases until they're truly fixed. - The ability to frame problems from scratch and work without strict handoffs - building from a goal, not a ticket. Qualifications Required: - You've built systems, not just prototypes. - You care about both ML results and the system's behavior in production. - You're comfortable taking a rough business goal and shaping the technical path to get there. - You're energized by product-focused AI work - things that users feel and rely on. - You've worked in or want to work in a startup-grade environment: messy, fast, and impactful. (Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well). If you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you! Role Overview: - Build and own AI-backed features end to end, from ideation to production including layout logic, smart cropping, visual enhancement, out-painting, and GenAI workflows for background fills. - Design scalable APIs that wrap vision models like BiRefNet, YOLOv8, Grounding DINO, SAM, CLIP, ControlNet, etc., into batch and real-time pipelines. - Write production-grade Python code to manipulate and transform image data using NumPy, OpenCV (cv2), PIL, and PyTorch. - Handle pixel-level transformations from custom masks and color space conversions to geometric warps and contour ops with speed and precision. - Integrate models into the production web app (AWS-based Python/Java backend) and optimize them for latency, memory, and throughput. - Frame problems when specs are vague - help define what good looks like and then build it. - Collaborate with product, UX, and other engineers without relying on formal handoffs - you own your domain. Key Responsibilities: - 23 years of hands-on experience with vision and image generation models such as YOLO, Grounding DINO, SAM, CLIP, Stable Diffusion, VITON, or TryOnGAN including experience with inpainting and outpainting workflows using Stable Diffusion pipelines. - Strong hands-on knowledge of NumPy, OpenCV, PIL, PyTorch, and image visualization/debugging techniques. - 12 years of experience working with popular LLM APIs such as OpenAI, Anthropic, Gemini, and how to compose multi-modal pipelines. - Solid grasp of production model integration - model loading, GPU/CPU optimization, async inference, caching, and batch processing. - Experience solving real-world visual problems like object detection, segmentation, composition, or enhancement. - Ability to debug and diagnose visual output errors - e.g., weird segmentation
