GeBBS - Technical Architect - Data Science

Role and Responsibilities : - Solution Design : Architect the development of AI solutions from conceptualization to deployment. - Leadership : Lead the development, deployment, and optimization of machine learning (ML), natural language processing (NLP) models, and LLM/GenAI solutions. - Pipeline Development : Develop and maintain Python-based ML pipelines and manage associated databases. - Model Deployment : Deploy AI models on Azure/AWS, ensuring optimal performance and scalability. - Mentorship : Stay updated on advancements in ML, NLP, and GenAI, and mentor team members to enhance their skills and knowledge. - Best Practices : Implement LLMOps best practices for efficient management of the model lifecycle. Skillset Requirements : - Experience : 8 to 11 years of relevant experience in data science. - Programming : Minimum of 8 years of experience in Python programming. - Machine Learning/NLP : At least 8 years of experience in machine learning and natural language processing. - LLM and GenAI : A minimum of 2 years of experience with large language models (LLMs) and generative AI (GenAI). - Agentic AI : A minimum of 1 year of experience with Agentic AI. - Database Management : At least 8 years of experience with any database. - Cloud Deployment : 4+ years of knowledge in deploying ML models/Python applications on Azure or AWS. - LLMOps : Familiarity with LLMOps is mandatory. (ref:hirist.tech) Role and Responsibilities : - Solution Design : Architect the development of AI solutions from conceptualization to deployment. - Leadership : Lead the development, deployment, and optimization of machine learning (ML), natural language processing (NLP) models, and LLM/GenAI solutions. - Pipeline Development : Develop and maintain Python-based ML pipelines and manage associated databases. - Model Deployment : Deploy AI models on Azure/AWS, ensuring optimal performance and scalability. - Mentorship : Stay updated on advancements in ML, NLP, and GenAI, and mentor team members to enhance their skills and knowledge. - Best Practices : Implement LLMOps best practices for efficient management of the model lifecycle. Skillset Requirements : - Experience : 8 to 11 years of relevant experience in data science. - Programming : Minimum of 8 years of experience in Python programming. - Machine Learning/NLP : At least 8 years of experience in machine learning and natural language processing. - LLM and GenAI : A minimum of 2 years of experience with large language models (LLMs) and generative AI (GenAI). - Agentic AI : A minimum of 1 year of experience with Agentic AI. - Database Management : At least 8 years of experience with any database. - Cloud Deployment : 4+ years of knowledge in deploying ML models/Python applications on Azure or AWS. - LLMOps : Familiarity with LLMOps is mandatory. (ref:hirist.tech)