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.
Analyze the current multimodal indexing pipeline to identify performance bottlenecks (latency scalability and throughput).
Design and implement GenAI-driven optimizations for data ingestion preprocessing embedding generation 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.