Overview

Role responsibilities

Responsibilities

• Develop and optimize LLM models using state-of-the-art techniques, focusing on RAG (Retrieval-Augmented Generation).
• Implement content extraction methods to enhance the accuracy and relevance of generated responses.
• Collaborate with cross-functional teams to identify and address LLM development and deployment challenges.
• Stay updated with the latest advancements in natural language processing (NLP) and machine learning research.
• Provide technical guidance and mentorship to junior team members.
• Contribute to documenting and disseminating best practices in LLM development and deployment.
• Implement RPA solutions to automate repetitive tasks and improve operational efficiency.
• Collaborate with data scientists and business analysts to understand data requirements and translate them into technical solutions.
• Monitor performance, troubleshoot issues, and optimize data workflows for scalability and reliability
• Stay up-to-date with the latest trends and technologies in data engineering, RPA, and AI/ML
• Bachelors or higher degree in Computer Science, Engineering, or a related field.
• Proven experience in developing and fine-tuning LLM models, preferably with a focus on RAG.
• Hands-on experience with RPA tools such as UiPath, Automation Anywhere, Blue Prism, Power Automate
• Strong proficiency in Python programming and familiarity with relevant libraries/frameworks such as Tensor Flow, PyTorch, scikit-learn.
• Solid understanding of natural language processing (NLP) fundamentals and machine learning principles.

Qualifications

• Familiarity with the concept of local LLMs and their applications in specific domains or languages.
• Excellent problem-solving skills and the ability to work effectively in a cooperative team environment.
• Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders
• Prior working knowledge in implementing RPA and AI/ ML Methods.
• Certification in RPA or AI/ML technologies.
• Contributions to open-source projects related to natural language processing or machine learning.
• Knowledge of big data technologies (e.g., Hadoop, Spark, Kafka) .