<p><p><b>Description : </b><br/><br/>We are looking for a passionate and skilled AI Engineer to join our AI/ML team.
The ideal candidate will have strong hands-on experience in Python, Machine Learning, Natural Language Processing (NLP), and Retrieval-Augmented Generation (RAG) techniques.
You will work on designing, developing, and deploying intelligent models and AI-driven applications in real-world : </b><br/><br/></p><p>- Design, develop, and deploy AI and ML models to solve business and product challenges.<br/><br/></p><p>- Build and optimize NLP pipelines for tasks such as text classification, entity extraction, and sentiment analysis.<br/><br/></p><p>- Implement Retrieval-Augmented Generation (RAG) architectures using LLMs and vector databases.<br/><br/></p><p>- Fine-tune pre-trained Large Language Models (LLMs) for domain-specific applications.<br/><br/></p><p>- Develop and integrate AI solutions into production systems using Python APIs or web frameworks.<br/><br/></p><p>- Collaborate with data engineers, backend developers, and product teams to ensure scalable AI deployment.<br/><br/></p><p>- Research and experiment with emerging AI/ML tools, frameworks, and architectures to improve model performance.<br/><br/></p><p>- Evaluate and monitor AI model performance using key metrics (accuracy, F1-score, perplexity, etc.).<br/><br/><b>Requirements : </b><br/><br/></p><p>- 4-8 years of hands-on experience in AI/ML model development and deployment.<br/><br/></p><p>- Strong programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers.<br/><br/></p><p>- Experience with Natural Language Processing (NLP) tokenization, embeddings, and transformer-based architectures (BERT, GPT, etc.).<br/><br/></p><p>- Practical knowledge of Retrieval-Augmented Generation (RAG) using tools like LangChain, LlamaIndex, or FAISS.<br/><br/></p><p>- Proficiency in vector databases (Pinecone, ChromaDB, Weaviate, Milvus, etc.).<br/><br/></p><p>- Strong understanding of data preprocessing, feature engineering, and model evaluation.<br/><br/></p><p>- Experience in cloud deployment (AWS, GCP, or Azure) and containerization (Docker).<br/><br/></p><p>- Experience working with LLM APIs (OpenAI, Anthropic, Mistral, etc.).<br/><br/></p><p>- Familiarity with knowledge graph construction or semantic search systems.<br/><br/></p><p>- Understanding of data pipelines and ETL workflows.<br/><br/></p><p>- Knowledge of API development or backend integration with Flask/FastAPI.</p><br/></p> (ref:hirist.tech)