NLP Engineer - Job DescriptionJob Title Natural Language Processing (NLP) Engineer Job Summary We are looking for an NLP Engineer to develop, train, and deploy language-based AI models. The ideal candidate will have experience with machine learning, deep learning, and modern NLP techniques to build applications such as chatbots, document analysis systems, translation tools, search engines, and AI assistants.
Key Responsibilities - Design and implement NLP models for text classification, sentiment analysis, named entity recognition (NER), summarization, question answering, and text generation.
- Fine-tune and evaluate transformer-based models (e.g., BERT, RoBERTa, T5, Llama).
- Preprocess and clean large text datasets.
- Build data pipelines for NLP applications.
- Deploy NLP models using REST APIs or cloud services.
- Optimize model accuracy, latency, and scalability.
- Collaborate with data scientists, software engineers, and product teams.
- Monitor deployed models and improve performance over time.
- Stay up to date with the latest NLP research and tools.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Strong programming skills in Python.
- Good understanding of machine learning and deep learning concepts.
- Knowledge of NLP techniques and transformer architectures.
- Experience working with large datasets.
- Familiarity with software development best practices, version control, and testing.
Required Skills Programming NLP Libraries - Hugging Face Transformers
- spaCy
- NLTK
- Gensim
Machine Learning & Deep Learning - PyTorch or TensorFlow
- Scikit-learn
- Transformer models
- Embeddings
LLM & Generative AI - Prompt engineering
- Retrieval-Augmented Generation (RAG)
- Vector databases (e.g., FAISS, Pinecone, ChromaDB)
- Model fine-tuning
- LangChain or LlamaIndex
Cloud & Deployment - Docker
- Kubernetes (preferred)
- AWS, Azure, or Google Cloud
- FastAPI or Flask
Preferred Qualifications - Experience with multilingual NLP.
- Knowledge of speech-to-text or text-to-speech systems.
- Familiarity with MLOps practices.
- Contributions to open-source NLP projects or research publications.
Soft Skills - Problem-solving
- Communication
- Team collaboration
- Analytical thinking
- Attention to detail
Nice-to-Have - Experience with LLM APIs (e.g., OpenAI, Anthropic, Gemini).
- Knowledge of vector search and semantic search.
- Experience deploying models in production environments.
- Understanding of AI evaluation metrics and responsible AI practices.
Sample Interview Topics - Python coding
- NLP fundamentals (tokenization, stemming, lemmatization, POS tagging)
- Transformer architecture and attention mechanisms
- BERT vs. GPT
- Word embeddings (Word2Vec, GloVe, FastText)
- Prompt engineering
- RAG architecture
- Model evaluation metrics (Precision, Recall, F1-score, BLEU, ROUGE)
- ML system design
- API development using FastAPI or Flask
- SQL and database fundamentals
