• Develop and fine-tune LLMs and NLP models using Hugging Face Transformers, OpenAI APIs, and LangChain.
• Implement retrieval-augmented generation (RAG) for intelligent AI-driven question answering and chatbot applications.
• Build and deploy AI models using FastAPI, Flask, and cloud-based inference engines (Azure ML, AWS SageMaker, GCP AI Platform).
• Optimize embedding search and vector retrieval using FAISS, Pinecone, and ANN-based search algorithms.
• Work on AI model deployment, API integration, and real-time AI application development in production environments.

Requirements

• Bachelor's or Master's degree in Artificial Intelligence, Computer Science, Data Science, or a related field.
• Atleast 2-4 years of experience in AI/ML development, NLP, or LLM-based application engineering.
• Proficiency in Python, with experience in AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
• Strong experience with OpenAI APIs, LangChain, and fine-tuning LLMs for domain-specific applications.
• Expertise in developing and deploying AI-powered applications using FastAPI, Flask, or Django.
• Hands-on experience with cloud-based AI services such as Azure ML, AWS SageMaker, or GCP AI Platform.
• Knowledge of retrieval-augmented generation (RAG) and its implementation for AI-driven automation.
• Proficiency in working with vector databases like FAISS, Pinecone, and ChromaDB for efficient search and retrieval.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes for scalable AI deployment.
• Strong problem-solving skills with the ability to troubleshoot and optimize AI models for realworld performance.
• Excellent communication and teamwork skills to collaborate with AI researchers, engineers, and business teams.