AI Engineer
2 days ago
We're seeking an experienced AI Engineer with deep expertise in open-source Large Language Models (LLMs) to join our team. In this role, you'll be responsible for training, fine-tuning, and deploying state-of-the-art language models while pushing the boundaries of what's possible with open-source AI.
Key Responsibilities
- Lead the implementation and optimization of open-source LLMs including LLAMA, Mistral, and DeepSeek models
- Design and execute model training pipelines, including pre-training and fine-tuning strategies
- Develop and implement efficient deployment strategies for LLMs across various infrastructure environments
- Optimize model performance, focusing on inference speed, memory usage, and computational efficiency
- Create and maintain MLOps pipelines for continuous model improvement and deployment
- Collaborate with cross-functional teams to integrate LLM capabilities into production applications
- Research and evaluate emerging open-source models and techniques to keep our technology stack current
Required Qualifications
- Demonstrated experience training and fine-tuning open-source LLMs (LLAMA, Mistral, DeepSeek)
- Strong proficiency in PyTorch and transformers libraries
- Expertise in distributed training techniques and optimization methods
- Experience with quantization, pruning, and other model optimization techniques
- Strong understanding of MLOps practices and deployment frameworks
- Proficiency in Python and software development best practices
Technical Skills
- Deep understanding of transformer architecture and attention mechanisms
- Experience with training frameworks: DeepSpeed, Accelerate, FSDP
- Familiarity with quantization techniques: QLoRA, GPTQ, GGUF
- Knowledge of deployment solutions: vLLM, text-generation-inference
- Experience with MLOps tools: Weight & Biases, MLflow, DVC
- Proficiency in containerization and orchestration: Docker, Kubernetes
- Understanding of prompt engineering and instruction tuning
Preferred Qualifications
- Experience contributing to open-source LLM projects
- Publications in NLP, LLM, or related fields
- Experience with multi-modal models
- Knowledge of model evaluation frameworks and metrics
- Familiarity with RAG (Retrieval-Augmented Generation) implementations
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