Generative AI/LLM Engineer

Job Description :Key Responsibilities :- Design and develop application solutions using generative models, RAG and vector database and vector search.- Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.- Research and stay current on the latest advancements in generative AI technologies, methodologies, and best practices.- Optimize and fine-tune generative models for performance, scalability, and efficiency.- Troubleshoot and resolve issues related to generative AI models, implementations, and workflows.- Create and maintain comprehensive documentation for generative AI models and their applications.- Build efficient data pipelines and manage large datasets for model training and evaluation.- Measure model outputs using appropriate metrics, with awareness of bias and fairness issues.- Implement and use AI coding assistants (e.g., GitHub Copilot) and version control (Git).- Conduct rigorous testing and build scalable, maintainable systems.- Read, analyze, and implement recent AI research papers; conduct experiments as needed.- Communicate complex technical concepts and findings to non-technical stakeholders.Required Qualifications : - Strong proficiency in Python and effective prompt engineering techniques; familiarity with other programming languages is a plus.- Hands-on experience with leading generative text models (e.g., Claude, OpenAI GPT, Gemini), including model fine-tuning and customization.- Proficiency in AWS Bedrock, including model access and knowledge base implementations; experience with Azure OpenAI.- Solid experience with AWS serverless architecture; familiarity with Azure or GCP is desirable.- Experience building and optimizing data pipelines for handling large-scale datasets.- Knowledge of metrics for evaluating model performance, including bias and fairness considerations.- Experience with AI coding assistants (e.g., GitHub Copilot), version control systems (Git), and scalable system design.- Excellent documentation skills and experience collaborating with multi-disciplinary teams.- Strong communication skills, with the ability to present technical concepts to non-technical audiences. (ref:hirist.tech) Job Description :Key Responsibilities :- Design and develop application solutions using generative models, RAG and vector database and vector search.- Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems.- Research and stay current on the latest advancements in generative AI technologies, methodologies, and best practices.- Optimize and fine-tune generative models for performance, scalability, and efficiency.- Troubleshoot and resolve issues related to generative AI models, implementations, and workflows.- Create and maintain comprehensive documentation for generative AI models and their applications.- Build efficient data pipelines and manage large datasets for model training and evaluation.- Measure model outputs using appropriate metrics, with awareness of bias and fairness issues.- Implement and use AI coding assistants (e.g., GitHub Copilot) and version control (Git).- Conduct rigorous testing and build scalable, maintainable systems.- Read, analyze, and implement recent AI research papers; conduct experiments as needed.- Communicate complex technical concepts and findings to non-technical stakeholders.Required Qualifications : - Strong proficiency in Python and effective prompt engineering techniques; familiarity with other programming languages is a plus.- Hands-on experience with leading generative text models (e.g., Claude, OpenAI GPT, Gemini), including model fine-tuning and customization.- Proficiency in AWS Bedrock, including model access and knowledge base implementations; experience with Azure OpenAI.- Solid experience with AWS serverless architecture; familiarity with Azure or GCP is desirable.- Experience building and optimizing data pipelines for handling large-scale datasets.- Knowledge of metrics for evaluating model performance, including bias and fairness considerations.- Experience with AI coding assistants (e.g., GitHub Copilot), version control systems (Git), and scalable system design.- Excellent documentation skills and experience collaborating with multi-disciplinary teams.- Strong communication skills, with the ability to present technical concepts to non-technical audiences. (ref:hirist.tech)