Description

You will build and optimize GenAI workflows focused on Retrieval-Augmented Generation (RAG) and Agentic AI.

Responsibilities

• Build and optimize RAG and Agentic AI workflows using LangChain and vector databases (Pinecone, FAISS, ChromaDB).
• Implement LLM fine-tuning, prompt chaining, and response ranking to improve generative AI performance.
• Develop and evaluate retrieval mechanisms using semantic search and vector similarity techniques.
• Benchmark model performance using BLEU, ROUGUE, METEOR, BERTScore, and other LLM evaluation metrics.
• Deploy AI models in production environments (AWS, GCP, Azure) following MLOps practices for monitoring and retraining.

Required Skills

• 2+ years of hands-on experience in Generative AI, RAG, and Agentic AI workflows.
• Expertise in LangChain, embedding models, and vector databases (Pinecone, FAISS, ChromaDB).
• Strong Python skills with experience in Hugging Face, PyTorch, TensorFlow, and scikit-learn.
• Proficiency in benchmarking LLMs using BLEU, ROUGUE, METEOR, and BERTScore.
• Experience deploying models on AWS, GCP, or Azure, utilizing MLOps tools.
• Familiarity with containerized deployments (Docker, Kubernetes) and distributed computing.
• Ability to optimize LLM pipelines for latency and efficiency.

Key Skills

Education

Any Graduate