Must-Have Skills
Core competencies required to fulfill the role:
Deep NLP Expertise
Hugging Face Transformers (BERT, GPT, etc.)
NLTK
Knowledge Graphs & Semantic Search
AI/ML Frameworks
PyTorch, TensorFlow, Keras
Python Programming (for NLP and ML model development)
Model Deployment & Optimization
Building production-ready NLP solutions
Good-to-Have Skills
Enhances capability and accelerates innovation:
Generative AI & LLM Fine-Tuning
Prompt engineering, RAG (Retrieval-Augmented Generation)
API Development
RESTful APIs for NLP services
MLOps Practices
Model versioning, monitoring, retraining pipelines
Cloud Platforms
GCP or Azure for scalable NLP deployments
Distributed & Parallel Processing
Ability to design and optimize algorithms for large-scale NLP tasks
CI/CD for NLP Pipelines
Jenkins, GitHub Actions for ML workflows
Trainable Skills (COE Enablement)
Skills that can be developed internally:
Performance Optimization
Model compression, quantization for faster inference
Containerization
Docker, Kubernetes for NLP model deployment

Keyskills: Huggingface NLP MLOPs Aiml Transformers RAG