Natural Language Processing Engineer Needed for AI Chatbot and Text Analytics

We're seeking an experienced Natural Language Processing Engineer to develop an intelligent conversational AI system with advanced text understanding, sentiment analysis, and domain-specific knowledge retrieval capabilities.

Project Overview:

Build an enterprise-grade AI chatbot with natural language understanding, intent classification, entity extraction, and contextual response generation. The system should integrate with existing databases and provide accurate, context-aware answers using RAG (Retrieval-Augmented Generation) architecture.

Key Responsibilities:

Design and develop NLP-based conversational AI systems

Implement intent classification and entity recognition models

Build question-answering systems with context understanding

Develop sentiment analysis and emotion detection modules

Create text summarization and information extraction pipelines

Implement RAG (Retrieval-Augmented Generation) architecture

Fine-tune large language models (LLMs) for specific domains

Build semantic search and document retrieval systems

Develop multi-turn dialogue management systems

Create text preprocessing and data cleaning pipelines

Implement multilingual NLP capabilities

Deploy models via REST APIs for production use

Required Skills:

3+ years of NLP and machine learning experience

Strong proficiency in Python and NLP libraries

Experience with transformer models (BERT, GPT, T5, RoBERTa)

Knowledge of NLP frameworks (spaCy, NLTK, Hugging Face Transformers)

Experience with LLM fine-tuning and prompt engineering

Understanding of vector databases (Pinecone, Weaviate, ChromaDB)

Experience with embeddings (Word2Vec, GloVe, sentence transformers)

Knowledge of dialogue systems and conversational AI

API development experience (FastAPI, Flask)

Cloud platform experience (AWS, GCP, Azure)

Technical Stack:

Languages: Python 3.8+

NLP Libraries: spaCy, NLTK, Hugging Face Transformers

ML Frameworks: PyTorch, TensorFlow, scikit-learn

LLMs: GPT-3.5/4, BERT, RoBERTa, T5, LLaMA

Vector Databases: Pinecone, Weaviate, ChromaDB, FAISS

Embeddings: OpenAI Embeddings, sentence-transformers

Frameworks: LangChain, LlamaIndex

API Development: FastAPI, Flask

Deployment: Docker, Kubernetes, AWS Lambda

Databases: PostgreSQL, MongoDB, Elasticsearch

NLP Tasks to Implement:

Intent classification and slot filling

Named Entity Recognition (NER)

Sentiment analysis and emotion detection

Text classification and categorization

Question answering (extractive and abstractive)

Text summarization

Language translation

Semantic similarity and search

Topic modeling

Text generation and completion

Chatbot Features:

Natural conversation flow management

Multi-turn dialogue with context retention

Contextual response generation

Fallback and clarification mechanisms

Small talk and chitchat capabilities

Personality and tone customization

Multi-intent handling

Disambiguation strategies

Confidence scoring and uncertainty handling

Human handoff integration

RAG Architecture:

Document chunking and preprocessing

Semantic embedding generation

Vector database integration

Similarity search and retrieval

Context-aware response generation

Source attribution and citations

Retrieval optimization strategies

Hybrid search (keyword + semantic)

Model Fine-tuning:

Domain-specific dataset preparation

Transfer learning from pre-trained models

Few-shot and zero-shot learning

Parameter-efficient fine-tuning (LoRA, QLoRA)

Prompt engineering and optimization

Model evaluation and validation

Hyperparameter tuning

Text Processing Pipeline:

Text cleaning and normalization

Tokenization and lemmatization

Stop word removal

POS (Part-of-Speech) tagging

Dependency parsing

Coreference resolution

Spelling correction

Language detection

Integration Requirements:

REST API for chatbot integration

Webhook support for messaging platforms

Knowledge base integration

CRM/database connectivity

Authentication and authorization

Rate limiting and caching

Logging and monitoring

Analytics and conversation tracking

Performance Metrics:

Intent classification accuracy (>90%)

Entity extraction F1 score (>85%)

Response latency (<2 seconds)

Contextual relevance score

User satisfaction ratings

Conversation completion rate

Fallback frequency

Multilingual Support:

Multiple language models

Cross-lingual embeddings

Language detection

Translation integration

Culture-specific responses

Deliverables:

Fully functional NLP chatbot system

Fine-tuned models with documentation

RAG pipeline implementation

Intent and entity training data

REST API with comprehensive documentation

Vector database setup and configuration

Model evaluation reports and metrics

Data preprocessing scripts

Deployment configuration (Docker/K8s)

Integration guide for web/mobile apps

Admin dashboard for model management

User conversation analytics

Technical documentation

Post-deployment support (2 weeks)

Use Cases:

Customer support automation

FAQ answering system

Document search and retrieval

Knowledge management

Virtual assistant

Meeting summarization

Content generation

Budget: $55 - $110/hour (Hourly) or $12,000 - $25,000 (Fixed project)

Timeline: 8-14 weeks