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About the role
Role Overview: At Uplevyl, we are redefining what intelligent communities look like by building scalable, agentic community systems through AI-powered agents. We are seeking an AI Engineer who understands community dynamics and can leverage their expertise to bring the next generation of agentic communities to life. Key Responsibilities: - Design, develop, and deploy LLM-based AI solutions tailored for scalable community systems. - Implement advanced RAG models, embedding pipelines, and vector databases (e.g., Pinecone, FAISS, Qdrant). - Work with multi-agent orchestration frameworks to build adaptive, intelligent workflows. - Fine-tune, pre-train, and evaluate LLMs for domain-specific applications. - Integrate AI/ML systems with AWS services such as SageMaker, Bedrock, ECS, and Cognito (or equivalent). - Collaborate with product and community teams to understand real-world use cases and translate them into robust AI solutions. - Ensure ethical AI practices when handling domain-sensitive datasets. - Stay updated on the latest AI research and apply innovative approaches to improve scalability and efficiency. Qualifications: - Bachelor's/Master's in Computer Science, AI/ML, Data Science, or related field. - 4+ years of experience in LLM-based solutions, NLP, or AI system design. - Proven experience with RAG models, vector databases (Pinecone, FAISS, Qdrant), and embedding pipelines. - Strong expertise in Python, PyTorch/TensorFlow, LangChain, LangGraph, Hugging Face Transformers. - Familiarity with AWS AI/ML stack (SageMaker, Bedrock, ECS, Cognito) or equivalent. - Experience in handling domain-sensitive datasets and applying ethical AI practices. - Hands-on experience with fine-tuning, pre-training, and evaluation of LLMs. Role Overview: At Uplevyl, we are redefining what intelligent communities look like by building scalable, agentic community systems through AI-powered agents. We are seeking an AI Engineer who understands community dynamics and can leverage their expertise to bring the next generation of agentic communities to life. Key Responsibilities: - Design, develop, and deploy LLM-based AI solutions tailored for scalable community systems. - Implement advanced RAG models, embedding pipelines, and vector databases (e.g., Pinecone, FAISS, Qdrant). - Work with multi-agent orchestration frameworks to build adaptive, intelligent workflows. - Fine-tune, pre-train, and evaluate LLMs for domain-specific applications. - Integrate AI/ML systems with AWS services such as SageMaker, Bedrock, ECS, and Cognito (or equivalent). - Collaborate with product and community teams to understand real-world use cases and translate them into robust AI solutions. - Ensure ethical AI practices when handling domain-sensitive datasets. - Stay updated on the latest AI research and apply innovative approaches to improve scalability and efficiency. Qualifications: - Bachelor's/Master's in Computer Science, AI/ML, Data Science, or related field. - 4+ years of experience in LLM-based solutions, NLP, or AI system design. - Proven experience with RAG models, vector databases (Pinecone, FAISS, Qdrant), and embedding pipelines. - Strong expertise in Python, PyTorch/TensorFlow, LangChain, LangGraph, Hugging Face Transformers. - Familiarity with AWS AI/ML stack (SageMaker, Bedrock, ECS, Cognito) or equivalent. - Experience in handling domain-sensitive datasets and applying ethical AI practices. - Hands-on experience with fine-tuning, pre-training, and evaluation of LLMs.