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Artificial Intelligence/Machine Learning Engineer
2 months ago
Role Overview :
We're on the lookout for a versatile and skilled AI and Machine Learning Specialist to join our dynamic team.
Our ideal candidate will bring expertise across various domains, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Conversational Chatbots, Langchain, RASA Chatbot Framework, AWS SageMaker, AWS Bedrock, and general Machine Learning.
As a key member of our team, you'll be tasked with the design, implementation, and optimization of advanced AI and machine learning models and systems.
Responsibilities:
- Participate in fine-tuning pre-trained models and evaluating performance.
- Assist in designing and implementing NLP and machine learning models.
- Support the development and integration of RAG, Chatbots, and various business use cases.
- Help design and implement chatbot functionalities, including conversation flows and dialogue management.
- Integrate external APIs and services into various applications.
- Assist in managing SageMaker environments and deploying foundation models using AWS Bedrock.
- Monitor and optimize model performance, ensuring data security and compliance.
- Collaborate with senior team members, data scientists, UX designers, and engineers on various projects.
Required Skills :
- Proficiency in Python and relevant libraries.
- Understanding of Large Language Models and their architecture.
- Knowledge of preparing datasets, fine-tuning pre-trained models, and evaluating/improving LLM performance.
- Familiarity with Retrieval Augmented Generation (RAG) and improving retrieval algorithms for better document selection.
- Experience with chatbot frameworks (RASA, Dialogflow) for building and training RASA models.
- Experience in Python framework(Flask/FastAPI)
- Familiarity with Langchain framework to integrate with various LLMs based on use cases.
- Proficiency in AWS Services (Sagemaker, Bedrock).
- Experience with data cleaning/preparation, training machine learning models, evaluating model performance, improving model results, and deploying models
