Aditya Pinnika

I am a dedicated and analytical professional with a background in Artificial Intelligence and Data Science, having completed my graduation from Ramachandra College of Engineering (RCEE). I began my career as a Business Analyst at KJ Systems, a healthcare domain company, where I was actively involved in bridging the gap between business needs and technical implementation. In this role, I gained hands-on experience in understanding client requirements, preparing detailed documentation, validating system workflows, and collaborating with cross-functional teams to ensure high-quality product delivery.

Working in the healthcare sector gave me valuable exposure to complex systems and critical workflows, such as medication administration records (MAR), inventory tracking, and patient chart management. I consistently worked to ensure compliance, accuracy, and user satisfaction, while maintaining a structured approach to testing and validation. This experience sharpened my attention to detail and enhanced my problem-solving capabilities.

Currently, I am working at Amazon, where I continue to uphold high standards of efficiency and precision. My role involves working with large datasets and operational workflows, where accuracy, speed, and accountability are key. Amazon's dynamic environment has further strengthened my ability to adapt quickly, manage time effectively, and work independently or as part of a team.

Alongside my core roles, I possess strong expertise in machine learning. I have built and evaluated various ML models, with experience in data preprocessing, feature engineering, and model optimization. I’m proficient in Python and commonly used libraries such as Scikit-learn, Pandas, NumPy, and TensorFlow. My understanding of supervised and unsupervised learning methods allows me to apply intelligent solutions to real-world problems.

I also specialize in data annotation, a crucial component in building reliable AI systems. I have worked extensively on annotation tasks involving image, text, and audio data. My ability to maintain accuracy and consistency over large volumes of annotations makes me a reliable contributor to any AI training pipeline.

With a blend of business analysis, machine learning, and data annotation experience, I bring a unique combination of technical and analytical skills. I’m passionate about contributing to data-driven projects, enhancing user experience, and supporting innovation through smart, scalable solutions. I look forward to opportunities where I can continue growing and adding value across domains.