Shirisha y

About Shirisha y

As a Senior AI/ML Engineer with over 10 years of experience, I specialize in developing and deploying innovative AI solutions that leverage the full potential of cloud computing, machine learning, and generative AI. My career is built on a strong foundation in Python, cloud infrastructure, and cutting-edge AI technologies, including GPT-3/4, BERT, LangChain, and TensorFlow. I am dedicated to delivering high-impact solutions that improve business efficiency, automate decision-making, and enhance user experiences. Currently, at AbbVie, I lead the development and deployment of advanced machine learning models in the pharmaceutical sector, utilizing AWS and Google Cloud Platform (GCP). On AWS, I have designed and deployed scalable AI models using services such as Amazon S3, Lambda, EC2, DynamoDB, and SageMaker, ensuring optimal performance for both batch and real-time data processing. I have implemented AWS Glue for ETL tasks and utilized Amazon CloudWatch and AWS X-Ray for monitoring, logging, and ensuring the health of production systems. Additionally, I have developed custom machine learning pipelines using SageMaker Pipelines to streamline model training and deployment, improving overall model lifecycle management. At US Bank, I was heavily involved in leveraging AWS to optimize cloud infrastructure for AI/ML workflows. My work included utilizing AWS Lambda for serverless computing, Amazon S3 for scalable storage solutions, and Amazon EC2 for model training and deployment. I also implemented AWS API Gateway to enable seamless communication between services, and used IAM roles to ensure secure access control across multiple cloud resources. By automating the scaling of machine learning inference services using Kubernetes on AWS, I achieved a 25% improvement in resource utilization, ensuring efficient processing of financial data. In addition to AWS, I have experience with AWS SageMaker, which has been instrumental in enabling the deployment and monitoring of machine learning models in production environments. I have also utilized AWS Elastic Beanstalk for deploying web applications and APIs, ensuring smooth and reliable performance in the cloud. My work is driven by a passion for cloud technologies, automation, and continuous learning. I am focused on building secure, scalable AI solutions that can adapt to dynamic business environments, leveraging both AWS and GCP to deliver high-performance applications that align with industry standards and regulations.

Experience

1. Lead Python Ai Ml Developer

   AbbVie

   Feb 2023 — Present · Chicago, IL, US

   I have extensive experience in developing and deploying scalable AI/ML models across cloud platforms such as GCP and Azure, leveraging services like Vertex AI, BigQuery ML, and TensorFlow for model optimization. My expertise spans building end-to-end machine learning pipelines, developing intelligent retrieval systems with RAG frameworks, and implementing large language models (LLMs) for real-time financial advisory solutions. I have automated data processes using Python, Snowflake, and Dataproc, while also performing penetration testing to ensure secure, compliant applications. In addition, I have led the design of secure systems for handling sensitive financial data and integrated third-party APIs. Furthermore, I’ve worked with various big data tools and cloud technologies to streamline data lakes, enhance data governance with tools like Collibra ADI, and improve user engagement through UI design and A/B testing.

Education

• University of New Hampshire

  Master's degree

  2019 — 2020

• Osmania University

  Bachelor of Technology - BTech

  2009 — 2013