Harsh Ghadiya

About Harsh Ghadiya

Machine Learning Engineer with around 4 years of experience in designing, developing, and deploying scalable AI and ML solutions to drive data-driven decision-making. Proficient in Python, R, SQL, and MATLAB, leveraging TensorFlow, Keras, PyTorch, and Scikit-learn to build robust machine learning and deep learning models. Skilled in implementing supervised and unsupervised learning algorithms, including Regression, Random Forest, Logistic Regression, k-Means, Clustering, SVM, Classification, KNN, Neural Networks, and Collaborative Filtering, enhancing predictive analytics capabilities. Well-versed in data preprocessing, feature engineering, and NLP techniques using NLTK, OpenCV, Pandas, and SciPy, improving model accuracy and performance. Good background in data visualization using Tableau, Power BI, Matplotlib, and Seaborn, enabling clear communication of insights and trends. Experienced in cloud platforms such as AWS, Azure, GCP, and DataBricks, deploying scalable ML pipelines and optimizing computational resources. Proficient in SQL, MySQL, Oracle, and PostgreSQL, ensuring efficient database management and querying for analytical applications.

Experience

1. Machine Learning-ai Ml Engineer

   State Street

   Jan 2024 — Present

   Engineered full-stack AI solutions by integrating LLM-powered Retrieval-Augmented Generation (RAG) models, improving data extraction efficiency by 30% and enhancing real-time decision-making. • Developed & deployed AI inference pipelines on AWS ECS/Fargate, optimizing model execution time by 40% & reducing infrastructure costs. • Built scalable AI-driven backend systems in Python with PostgreSQL on AWS RDS, handling millions of structured and unstructured data points, improving query performance by 50%. • Optimized LLM-based AI Agents by implementing advanced prompt engineering techniques, improving response accuracy by 25% and reducing latency. • Designed and implemented a robust AI infrastructure using Docker, Kubernetes, and Terraform, enabling seamless deployment and scaling of AI models across cloud environments. • Developed internal AI-powered developer tools & automation frameworks in TypeScript and Vue/Nuxt, increasing engineering productivity by 35%. Enhanced cloud-based AI monitoring & logging systems using AWS CloudWatch & Prometheus, reducing downtime incidents by 30%. • Collaborated with cross-functional teams to scale AI solutions, aligning product goals with cutting-edge AI advancements, leading to a 15% increase in customer engagement.

Education

• Gujarat Technological University (GTU)

  Bachelor's degree

  2017 — 2021

• California State University, Northridge

  Master's degree

  2022 — 2024