Experience: 1 - 3 YearsOpenings: 2Package: as per industry standards
Job Summary:
Key Responsibilities:
Design and develop NLP models for Intent Classification and Entity Recognition.
Prepare, clean, and label conversational datasets.
Train, fine-tune, and evaluate machine learning models for conversational AI.
Build hybrid AI systems using ML-based intent detection with LLM fallback.
Develop conversation routing logic based on confidence scores.
Optimize model accuracy, latency, and inference performance.
Integrate ML models with Python backend services and REST APIs.
Design conversation flows for AI Voice Bots and Chatbots.
Work with Prompt Engineering for LLM fallback responses.
Build and maintain RAG (Retrieval-Augmented Generation) pipelines.
Monitor model performance and retrain models when required.
Collaborate with Backend, Frontend, and Product teams.
Required Skills:
Solid Python programming
Machine Learning
Natural Language Processing (NLP)
Intent Classification
Text Classification
Named Entity Recognition (NER)
scikit-learn
PyTorch or TensorFlow
Hugging Face Transformers
Sentence Transformers
Model Training & Fine-Tuning
Model Evaluation (Precision, Recall, F1-Score)
REST APIs
Git
Prompt Engineering
RAG
Vector Databases (Qdrant, Pinecone, Weaviate, Chroma)
LangChain / LangGraph / LlamaIndex
Valuable to Have:
Experience building AI Voice Bots
Hybrid ML + LLM architectures
Conversation Flow Design
Speech-to-Text (STT) and Text-to-Speech (TTS) integrations
Production deployment of ML models