GenAI Engineer

Role 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