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Niche NLP, RAG and vector DB skills plus non-metro location reduce applicant competition.
Specialized NLP/RAG skills increase domain bias but remain reasonably transferable across industries.
Explicit 1–3 years plus many mandatory ML/NLP tools and frameworks creates strict filters.
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Design, train, evaluate, and deploy NLP models for intent classification and entity recognition in AI Voice Agents and Chatbots.
Build and optimize hybrid AI systems combining ML intent detection with Large Language Model (LLM) fallback, including conversation routing logic based on confidence scores.
Integrate NLP models with Python backend services, REST APIs, and maintain RAG pipelines while monitoring and retraining models as needed.
1 to 3 years of relevant experience in Machine Learning with NLP focus.
Strong programming skills in Python.
Experience with ML frameworks: PyTorch or TensorFlow, and NLP libraries such as Hugging Face Transformers and Sentence Transformers.
Proficiency in building and evaluating models for Intent Classification, Named Entity Recognition, and working knowledge of REST APIs and Git.
Has hands-on experience developing conversational AI solutions including AI Voice Bots and Chatbots, especially hybrid ML + LLM architectures.
Skilled in prompt engineering techniques and leveraging vector databases (e.g., Qdrant, Pinecone) with LangChain or similar tools for building RAG pipelines.
Capable of end-to-end conversation flow design and operational deployment of ML models in production environments.