12 - 20 years
20 - 35 Lacs
Posted:15 hours ago|
Platform:
Work from Office
Full Time
Key Responsibilities:
Machine Learning (ML):
Design, build, and deploy ML pipelines for structured and unstructured data.
Conduct feature engineering, model selection, and hyperparameter tuning.
Evaluate model performance using industry-standard metrics and improve accuracy.
Collaborate with data engineering teams to ensure clean and accessible data for model training.
Apply supervised, unsupervised, and reinforcement learning techniques as per use case.
Natural Language Processing (NLP):
Develop NLP pipelines for text classification, sentiment analysis, entity recognition, summarization, etc.
Leverage libraries like spaCy, NLTK, Hugging Face Transformers, and Gensim.
Preprocess and tokenize large corpora using advanced NLP methods.
Implement solutions for multi-lingual, domain-specific text data challenges.
Integrate NLP services with applications or workflows.
Large Language Models (LLM):
Fine-tune and deploy LLMs (e.g., GPT, LLaMA, BERT, Falcon, Mistral) on custom datasets.
Use prompt engineering and retrieval augmented generation (RAG) to build intelligent systems.
Optimize inference performance and latency for production-level deployment.
Stay updated with the latest in GenAI, foundation models, and transformer architectures.
Work on use cases like chatbots, question answering, summarization, content generation, and semantic search.
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