Machine Learning Engineer (NLP / Conversational AI)

Triosoft technologies

Experience: 1 - 3 Years
Openings: 2
Package: as per industry standards

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