Pozent Corporation
Overview
Build and deploy generative AI solutions using LLM fine-tuning, RAG architectures, and agentic AI systems. Responsibilities
Fine-tune large language models for domain-specific applications and use cases Design and implement RAG (Retrieval Augmented Generation) pipelines with vector databases Build agentic AI systems with autonomous decision-making and tool-using capabilities Optimize LLM performance through prompt engineering, parameter tuning, and model selection Develop and deploy production-ready GenAI applications and APIs Evaluate model performance, conduct A/B testing, and iterate on solutions Collaborate with cross-functional teams to integrate GenAI into products Requirements
Bachelor\'s degree in Computer Science, Engineering, AI/ML, or related field Strong experience with LLM fine-tuning (LoRA, QLoRA, full fine-tuning, PEFT methods) Proven expertise building RAG systems with vector databases and embeddings Hands-on experience developing agentic AI solutions and multi-agent systems Proficiency with LLM frameworks (LangChain, LlamaIndex, Haystack) Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS) Strong Python programming skills Understanding of transformer architectures and LLM APIs Preferred
Experience with distributed training and GPU optimization Knowledge of MLOps, model deployment, and monitoring Familiarity with Hugging Face ecosystem Experience with prompt optimization and evaluation frameworks Understanding of LLM safety, alignment, and guardrails Background in NLP or deep learning research Seniority level
Entry level Employment type
Full-time Industries
IT Services and IT Consulting
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Build and deploy generative AI solutions using LLM fine-tuning, RAG architectures, and agentic AI systems. Responsibilities
Fine-tune large language models for domain-specific applications and use cases Design and implement RAG (Retrieval Augmented Generation) pipelines with vector databases Build agentic AI systems with autonomous decision-making and tool-using capabilities Optimize LLM performance through prompt engineering, parameter tuning, and model selection Develop and deploy production-ready GenAI applications and APIs Evaluate model performance, conduct A/B testing, and iterate on solutions Collaborate with cross-functional teams to integrate GenAI into products Requirements
Bachelor\'s degree in Computer Science, Engineering, AI/ML, or related field Strong experience with LLM fine-tuning (LoRA, QLoRA, full fine-tuning, PEFT methods) Proven expertise building RAG systems with vector databases and embeddings Hands-on experience developing agentic AI solutions and multi-agent systems Proficiency with LLM frameworks (LangChain, LlamaIndex, Haystack) Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS) Strong Python programming skills Understanding of transformer architectures and LLM APIs Preferred
Experience with distributed training and GPU optimization Knowledge of MLOps, model deployment, and monitoring Familiarity with Hugging Face ecosystem Experience with prompt optimization and evaluation frameworks Understanding of LLM safety, alignment, and guardrails Background in NLP or deep learning research Seniority level
Entry level Employment type
Full-time Industries
IT Services and IT Consulting
#J-18808-Ljbffr