AI Engineer
November 5
Description
• Build and optimize hybrid AI solutions leveraging Large Language Models (LLMs). • Collaborate with cross-functional teams to develop scalable solutions. • Design, develop, and deploy hybrid RAG architectures integrating LLMs with retrieval-based systems. • Fine-tune and optimize large language models, enhancing performance. • Implement and manage RAG pipelines combining retrieval mechanisms with generative capabilities. • Monitor and troubleshoot issues within RAG pipelines.
Requirements
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field, or equivalent practical experience. • 3+ years of experience in AI/NLP, with a focus on LLMs, transformer-based architectures, and retrieval systems. • Proven experience building and deploying RAG solutions or other hybrid AI architectures. • Strong understanding of information retrieval methods, including dense retrieval, sparse retrieval, and embeddings-based techniques. • Proficiency in Python, TensorFlow or PyTorch, and experience with libraries and tools related to LLMs, such as Hugging Face Transformers. • Familiarity with retrieval frameworks like Elasticsearch, FAISS, or OpenSearch. • Knowledge of prompt engineering, fine-tuning, and deployment of language models for production environments. • Strong analytical skills, with experience in optimizing LLM and retrieval model performance. • English required • Preferred Skills: Experience with cloud services and infrastructure (AWS, GCP, Azure) and MLOps tools for model deployment and monitoring. Contributions to open-source RAG projects or experience working with OpenAI, LangChain, or similar frameworks. Knowledge of vector databases, memory-augmented networks, and distributed systems.
Apply NowSimilar Jobs
October 10
Digibee
201 - 500
Design and implement generative AI models for Digibee's integration platform.
