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The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Requirements:
- Must have 5+ years of experience
- Strong proficiency in data engineering concepts and practices.
- Extensive experience in applying data science and machine learning techniques.
- Working knowledge of extracting information from unstructured data sources, particularly PDFs.
- Hands-on experience with Large Language Models (LLMs) such as GPT and Gemini.
- Knowledge of prompt engineering and fine-tuning techniques.
- Practical experience in building Retrieval-Augmented Generation (RAG) systems.
- Experience in processing large volumes of unstructured data, preferably in the insurance, legal, or healthcare sectors.
- Proven experience in extracting valuable insights from diverse data sets.
- Familiarity with Vector Databases, Azure Cloud, LangChain, Lama Index, and OCR tools and techniques.
- Working knowledge of PDF to text extraction tools like PDFMiner, PyMuPDF, or PDFPlumber, OCR tools.
- Skilled in machine learning, deep learning, computer vision, natural language processing (NLP), and generative AI models.
Good to have:
Working knowledge of Python and libraries such as NumPy, pandas, scikit-learn, and TensorFlow/PyTorch.
Preferred Qualifications:
BE/MS/PhD in Computer Science, Data Science, Machine Learning, or related fields.
Note: We are looking for passionate candidates who can join immediately.
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Since its inception, Appknox has become one of the go-to security solutions with the most powerful plug-and-play security platform, enabling security researchers, developers, and enterprises to build safe and secure mobile ecosystems using a system-plus human approach.
Appknox offers VA+PT solutions ( Vulnerability Assessment + Penetration Testing ) that provide end-to-end mobile application security and testing strategies to Fortune 500, SMB and Large Enterprises Globally helping businesses and mobile developers make their mobile apps more secure, thus not only enhancing protection for their customers but also for their own brand.
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