AI Product Engineer (Visual Intelligence and Automation)

Job Description

What You’ll Do ● 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 your models into our production web app (AWS based Python/Java backend) and optimize them for latency, memory, and throughput ● Frame problems when specs are vague — you’ll 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 What You’ll Need ● 2–3 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 (e.g., Diffusers, InvokeAI, or custom-built solutions) ● Strong hands-on knowledge of NumPy, OpenCV, PIL, PyTorch, and image visualization/debugging techniques. ● 1–2 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 (ECS, EC2/GPU, Lambda). ● Solid grasp of production model integration — model loading, GPU/CPU optimization, async inference, caching, and batch processing. ● A customer-centric approach — you think about how your work affects end users and product experience, not just model performance ● A quest for high-quality deliverables — you write clean, tested code and debug edge cases until they’re truly fixed ● The ability to frame problems from scratch and work without strict handoffs — you build from a goal, not a ticket