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Remote, popular AI/LLM role with broad requirements attracts many qualified applicants.
Core ML/AI skills transfer across industries, but specialized LLM/VLM experience increases domain specificity.
Many mandatory technical filters (LLM/VLM, fine-tuning, RAG, PyTorch, vector DBs, deployment).
Design, develop, and deploy production-grade AI applications leveraging Large Language Models (LLMs) and Vision-Language Models (VLMs).
Build and optimize Retrieval-Augmented Generation (RAG) pipelines including document ingestion, embeddings, retrieval, reranking, and generation components.
Develop multimodal AI solutions integrating text, images, documents, and implement APIs and production services for scalable deployment.
Proven hands-on experience with LLMs and/or VLMs and strong Python programming and software engineering skills.
Experience in building RAG systems, vector search solutions, and working knowledge of PyTorch and Hugging Face Transformers.
Familiarity with vector databases (FAISS, Milvus, Pinecone, Weaviate, pgvector) and cloud AI infrastructure (AWS/Azure/GCP).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced engineer with deep expertise in Transformer architectures, attention mechanisms, prompt engineering, and model fine-tuning.
Demonstrated ability to combine multimodal components (text, images, OCR, vision tasks) into cohesive AI pipelines.
Proficient in end-to-end development including API/service development, model serving optimization, and collaboration across ML and software engineering teams.