Chandrasekhara Reddy

• Designed and implemented data pipelines for generative AI models, leveraging Python and PyTorch for deep learning applications.
• Collaborated with cross-functional teams to integrate generative AI solutions into existing workflow systems, ensuring seamless deployment and scalability.
• Optimized and fine-tuned generative models for performance and efficiency, utilizing AWS Bedrock for model deployment.
• Developed LLM Ops pipelines to streamline the merge and deployment of changes in generative AI models.
• Conducted prompt engineering to enhance the performance of large language models (LLMs) for specific use cases.
• Researched and implemented the latest advancements in generative AI technologies, ensuring cutting-edge solutions.
• Troubleshot and resolved issues related to generative AI models, ensuring smooth operation in production environments.
• Created and maintained comprehensive documentation for generative AI models and their applications.
• Communicated complex technical concepts and findings to non-technical stakeholders, facilitating informed decision-making.
• Developed and deployed AI models on AWS using services like Lambda, S3, and EMR, ensuring scalability and performance.
• Utilized Python and PySpark for data processing and transformation, integrating machine learning models into data pipelines.
• Collaborated with data scientists to implement generative AI solutions, leveraging AWS Bedrock for model deployment.
• Built and maintained CI/CD pipelines for AI model deployment, ensuring efficient and secure updates.
• Conducted data analysis and visualization using Python libraries like Matplotlib and Seaborn to support AI model development.
• Worked with cross-functional teams to ensure alignment between technical solutions and business objectives.

Environment: Python, PyTorch, AWS Bedrock, LLM Ops, Prompt Engineering, Generative AI, MERN Stack, Git, CI/CD.