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.
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