Model Development Fine-Tuning: Develop, fine-tune, and evaluate LLMs, diffusion models, VLMs, and transformer-based architectures
Train models using custom datasets, including text, image, audio, and multimodal data Implement prompt engineering, instruction tuning, and retrieval-augmented generation (RAG) pipelines

Requirements

1 to 3 years experience in applied ML or generative AI
Strong proficiency in Python, PyTorch/TensorFlow, and HuggingFace ecosystem Practical experience with LLMs, diffusion models, or vision transformers

Preferred

Experience with OpenAI, Anthropic, Mistral, Llama, Qwen, or similar model families
Familiarity with vector databases (Pinecone, Weaviate, FAISS) Knowledge of RAG, agentic workflows, and LLM tool-use frameworks (LangChain, LlamaIndex) Exposure to multimodal models (e.g., CLIP, VLMs, SAM, diffusion models) Experience in medical imaging, radiology AI, or healthcare AI (bonus for Biocliq/Eliqsar context) Contributions to open-source ML projects or published research

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Generative AI Engineer

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Generative AI Engineer