Winmore
We're hiring a Senior AI Engineer who lives and breathes LLMs, deep learning frameworks, and end-to-end AI pipeline design. This role is for someone who can confidently move beyond fine-tuning chatbots and work with real-world GenAI challenges from embedding strategies to scalable deployments in production
Responsibilities
Design, build, and deploy LLM-based applications (e.g., summarization, document parsing, chat assistants). Develop scalable, repeatable AI pipelines for training, evaluation, and deployment. Fine-tune transformer models using PyTorch, TensorFlow, and Hugging Face Transformers. Implement retrieval-augmented generation (RAG) pipelines using FAISS or Weaviate. Deploy using FastAPI, Docker, Kubernetes, and GPU cloud infrastructure. Integrate prompt engineering and embeddings using LangChain or open-source LLMs. Support classic ML models (Scikit-learn, XGBoost) where needed. Requirements
4-8 years of experience in AI/ML with strong LLM/GenAI expertise. Strong Python skills and deep knowledge of TensorFlow, PyTorch, and Hugging Face. Experience in training and deploying transformer models. Familiarity with LLMOps, vector DBs, and cloud deployments. Working knowledge of classical ML techniques. LoRA/PEFT experience, agent frameworks like CrewAI or LangGraph. GitHub or publication record, experience with vision-language models. Experience with multi-modal models, NLP/NLU, or vision-language models (e.g., CLIP, Flamingo).
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Design, build, and deploy LLM-based applications (e.g., summarization, document parsing, chat assistants). Develop scalable, repeatable AI pipelines for training, evaluation, and deployment. Fine-tune transformer models using PyTorch, TensorFlow, and Hugging Face Transformers. Implement retrieval-augmented generation (RAG) pipelines using FAISS or Weaviate. Deploy using FastAPI, Docker, Kubernetes, and GPU cloud infrastructure. Integrate prompt engineering and embeddings using LangChain or open-source LLMs. Support classic ML models (Scikit-learn, XGBoost) where needed. Requirements
4-8 years of experience in AI/ML with strong LLM/GenAI expertise. Strong Python skills and deep knowledge of TensorFlow, PyTorch, and Hugging Face. Experience in training and deploying transformer models. Familiarity with LLMOps, vector DBs, and cloud deployments. Working knowledge of classical ML techniques. LoRA/PEFT experience, agent frameworks like CrewAI or LangGraph. GitHub or publication record, experience with vision-language models. Experience with multi-modal models, NLP/NLU, or vision-language models (e.g., CLIP, Flamingo).
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