GenAI Engineer

Top 3 must have skills AI/ML engineering with hands-on experience in multimodal models (CLIP, BLIP, Whisper, or similar models) Python vector databases (e.g., FAISS, Milvus, Weaviate) and embedding pipelines. Job Description Analyze the current multimodal indexing pipeline to identify performance bottlenecks (latency, scalability, and throughput). Design and implement GenAI-driven optimizations for data ingestion, preprocessing, embeddinggeneration, vector storage, and retrieval and indexing. Improve embedding quality and efficiency for diverse modalities (text, image, audio, video). Integrate and optimize vector databases / retrieval systems (e.g., Weaviate, FAISS, Milvus). Build scalable microservices/APIs for multimodal embedding and retrieval workflows. Collaborate with data scientists, ML engineers, and platform teams to streamline ETL and orchestration pipelines. Develop monitoring, logging, and alerting for indexing pipeline health and performance. Stay updated with emerging GenAI frameworks (OpenAI, Hugging Face, LangChain, LlamaIndex, etc.) and apply them to pipeline improvements. #J-18808-Ljbffr TechDigital Group