GenAI Engineer - NLP & Python

Salary : 22-28lpa Job Title : GenAI Engineer (NLP & Python) Experience : 7-10 Years Location : Remote / Onsite (As per company requirements) Job Type : Contract / Full-time Job Summary : We are looking for a Generative AI Engineer with expertise in Natural Language Processing (NLP) and Python to design, develop, and optimize AI models for text generation, language understanding, and conversational AI applications. The ideal candidate should have strong experience in deep learning, large language models (LLMs), fine-tuning, and deployment of NLP models in production environments. Key Responsibilities : Generative AI Model Development : - Design, train, and fine-tune Large Language Models (LLMs) using Transformer architectures (GPT, BERT, T5, LLaMA, Mistral, etc.). - Implement state-of-the-art NLP models for text generation, summarization, translation, and chatbot applications. - Optimize zero-shot, few-shot, and fine-tuned models for specific business use cases. - Develop embedding-based retrieval models for information extraction and semantic search. Natural Language Processing (NLP) & Text Analytics : - Implement and fine-tune Named Entity Recognition (NER), Sentiment Analysis, Text Classification, Topic Modeling, and Question Answering models. - Work with NLP libraries like Hugging Face Transformers, spaCy, NLTK, and Stanford NLP. - Optimize tokenization, embeddings, and text vectorization techniques (Word2Vec, FastText, Sentence-BERT). - Handle multilingual NLP processing and low-resource language adaptation. Machine Learning & Deep Learning : - Develop, train, and deploy deep learning models using PyTorch, TensorFlow, or JAX. - Use Reinforcement Learning with Human Feedback (RLHF) to improve model responses. - Implement fine-tuning techniques like LoRA, QLoRA, and PEFT (Parameter Efficient Fine-Tuning). - Optimize model performance using quantization, distillation, and pruning. Prompt Engineering & AI Optimization : - Develop advanced prompt engineering techniques to improve AI-generated outputs. - Implement Retrieval-Augmented Generation (RAG) with vector databases (FAISS, Pinecone, Weaviate). - Optimize context windows and chaining strategies for large-scale conversational AI. - Work on agent-based architectures (LangChain, LlamaIndex) for structured automation. Cloud & MLOps for AI Deployment : - Deploy GenAI models on AWS (SageMaker, Bedrock), Azure (OpenAI, ML Studio), or Google Cloud Vertex AI. - Use Docker, Kubernetes, and serverless frameworks for scalable AI solutions. - Build and manage ML pipelines using Kubeflow, MLflow, or TFX. - Implement monitoring, logging, and A/B testing for deployed AI models. Integration with Applications & APIs : - Build and expose AI models via RESTful and GraphQL APIs. - Integrate AI models with chatbots (Rasa, OpenAI API, LangChain) and enterprise applications. - Develop custom AI-powered assistants, virtual agents, and document automation tools. Required Skills & Qualifications : Programming & AI Development : - Strong Python programming skills (NumPy, Pandas, PyTorch, TensorFlow, JAX). - Experience with Transformer-based NLP models (GPT, BERT, T5, LLaMA, Mistral, Falcon). - Knowledge of Hugging Face Transformers, spaCy, NLTK, OpenAI API. - Expertise in data preprocessing, tokenization, and feature engineering for NLP. NLP & Deep Learning : - Hands-on experience in text generation, classification, summarization, and translation. - Strong understanding of self-attention mechanisms, transfer learning, and embeddings. - Familiarity with sequence modeling techniques (RNNs, LSTMs, Transformers). MLOps & AI Deployment : - Experience in deploying AI models on AWS, Azure, or GCP. - Proficiency in Docker, Kubernetes, and cloud-based AI model serving. - Knowledge of MLflow, Kubeflow, Airflow for ML pipeline automation. Prompt Engineering & AI Optimization : - Strong expertise in prompt tuning, context length optimization, and prompt chaining. - Experience with vector databases (FAISS, Pinecone, Weaviate) for Retrieval-Augmented Generation (RAG). - Ability to fine-tune LLMs using LoRA, QLoRA, PEFT techniques. Other Qualifications : - Bachelor's/Master's degree in Computer Science, AI, Machine Learning, or a related field. - 7-10 years of experience in NLP, Generative AI, or Data Science. Strong analytical, problem-solving, and communication skills. (ref:hirist.tech)

Location: in, IN

Posted Date: 4/19/2025