I am a passionate data scientist and machine learning engineer with a proven track record of delivering impactful solutions. My work experience aligns well with the requirements for the machine learning engineer role. At Nationwide, I designed and implemented a Retrieval Augmented Generation (RAG) system for question answering, enhancing its performance from 45% to 98% accuracy through various techniques. I constructed and deployed custom Docker images for data processing and model inference on AWS SageMaker, supporting both batch and real-time models. My role involved upgrading legacy deployments, achieving high accuracy in output alignment, and utilizing tools and frameworks such as Python, SQL, Cypher, LlamaIndex, Pinecone, Neo4j, PyTorch, Kubernetes, and Docker. This experience has given me a strong foundation in model deployment, automation, CI/CD practices, and cloud computing. At Openplay Technologies, I developed an end-to-end data pipeline, enhancing its scalability, extensibility, and fault tolerance, and built a data analytics platform using Elasticsearch, Spark, Hive, and various AWS services. These efforts led to significant improvements in player game joining time and user retention, contributing to the team’s annual revenue goals. My experience demonstrates my ability to develop and maintain scalable, efficient, and well-documented codebases, work with cloud computing and database systems, and build custom integrations. I have a proven track record of collaborating with data scientists, engineers, and stakeholders to achieve project goals, and I apply rigorous software engineering best practices to ensure the performance and reliability of machine learning systems.