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. 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.