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Data Scientist -NLP, Deep Learning, GenAI-(5 to 8 Years) at Enable Data Incorporated
, , India -
Full Time

Start Date

Immediate

Expiry Date

30 Jun, 26

Salary

0.0

Posted On

01 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, NLP, Generative AI, Transformer Models, Embeddings, Hugging Face, spaCy, NLTK, Python, PyTorch, TensorFlow, RAG Architectures, Vector Search, Prompt Optimization, MLOps, Model Deployment

Industry

IT Services and IT Consulting

Description
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.
Responsibilities
Develop end-to-end NLP and GenAI solutions, including text classification and conversational AI. Collaborate with product, engineering, and business teams to translate requirements into AI-driven solutions.