Develop and implement NLP and GenAI solutions, fine-tune LLM-based models, design workflows, and collaborate with teams to deliver AI solutions.
Key Responsibilities
- Develop end‑to‑end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines.
- Build, fine‑tune, and evaluate LLM-based models using transformer architectures (BERT, GPT, T5, LLaMA, etc.).
- Design and implement custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies.
- Develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures.
- Deploy models and AI apps using modern MLOps practices across cloud environments (Azure preferred).
- Collaborate closely with product, engineering, and business teams to translate requirements into AI-driven solutions.
- Monitor model performance, conduct error analysis, and continuously optimize pipelines.
Required Skills
- 5–8 years of experience in data science with deep hands‑on expertise in NLP and Generative AI.
- Proficient in transformer models, embeddings, and modern NLP libraries (Hugging Face, spaCy, NLTK).
- Strong Python skills with experience in PyTorch/TensorFlow for advanced model development.
- Practical experience building RAG architectures, vector search, and prompt optimization.
- Solid understanding of MLOps, model deployment, monitoring, and productionization.
- Strong problem‑solving abilities with excellent communication and stakeholder engagement skills.
Top Skills
Azure
Bert
Genai
Gpt
Hugging Face
Llama
Nlp
Nltk
Python
PyTorch
Spacy
T5
TensorFlow
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