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.

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.

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