Machine Learning Engineer (NLP / Conversational AI)

Job Summary

We are looking for a Machine Learning Engineer with experience in NLP and Conversational AI to build intelligent AI Voice Agents and Chatbot solutions. The candidate will be responsible for designing, training, evaluating, and deploying intent classification models, entity recognition systems, and hybrid AI pipelines combining Machine Learning with Large Language Models (LLMs).

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

• Strong 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

Good 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