AI Engineer

Responsibilities Build and extend backend services that power AI-driven media search and metadata enrichment. Develop, integrate, and deploy AI/ML inference pipelines (embeddings, vision/audio models, transcription, background removal, etc. ). Fine-tune and optimize computer vision and generative models (e. g., U2Net, BiRefNet, CLIP, Whisper, YOLO, diffusion models). Work with large datasets (100k-5M images): preprocessing, augmenting, and structuring for training/inference. Contribute to building pipelines for tasks like background removal, inpainting/outpainting, banner generation, logo/face detection, and multimodal embeddings. Integrate with vector databases (e. g., FAISS, Pinecone, Weaviate, Qdrant) for similarity and semantic search. Collaborate with the engineering team to deploy scalable AI inference endpoints (Docker + GPU/EC2/SageMaker).

Requirements Experience: 2-3 Years. Core Python (Required) - solid programming and debugging skills in production systems. AI/ML Libraries - hands-on experience with PyTorch and/or TensorFlow, NumNum, OpenCV, Hugging Face Transformers. Model Training/Fine-Tuning - experience fine-tuning pre-trained models for vision, audio, or multimodal tasks. Data Handling - preprocessing and augmenting image/video datasets for training and evaluation. Comfortable with chaining or orchestrating multimodal inference workflows (e. g., image + audio + OCR unified embedding).

Bonus Points If You Have worked with generative models (diffusion, inpainting, or outpainting). Understand large-scale media workflows (video, design files, time-coded metadata). Enjoy experimenting with new models and pushing them into production. Care about making AI useful in real-world creative pipelines. Vector Search - familiarity with FAISS, Pinecone, or similar for embeddings-based search.

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