Applied AI Engineering for Developers: Building Intelligent Applications at Scale

Applied AI Engineering for Developers: Building Intelligent Applications at Scale (eBook)

Description

Artificial Intelligence is no longer a distant vision confined to research laboratories-it is now a fundamental force shaping modern software systems. From recommendation engines and intelligent assistants to predictive analytics and autonomous systems, AI has become deeply integrated into the applications developers build every day. This transformation has created a growing need for professionals who not only understand algorithms but can also engineer scalable, reliable, and ethical AI-driven solutions. This book, Applied AI Engineering for Developers: Building Intelligent Applications at Scale, is written to address that need.
The purpose of this book is to bridge the gap between theoretical knowledge and practical implementation. While many resources focus either on mathematical foundations or high-level concepts, developers often require a more applied perspective-one that explains how to take models from experimentation to production. This book emphasizes that journey. It explores how data is prepared, how models are selected and trained, how systems are designed, and how AI solutions are deployed and maintained in real-world environments.
Throughout the chapters, the reader is guided step by step through the lifecycle of AI engineering. Beginning with foundational concepts, the book gradually moves into more advanced topics such as deep learning architectures, scalable infrastructure, and deployment strategies. Special attention is given to practical challenges faced by developers, including handling imperfect data, optimizing performance, integrating models into applications, and ensuring system reliability at scale. The goal is to equip readers with both conceptual clarity and actionable knowledge.
Equally important is the focus on responsibility and ethics. As AI systems increasingly influence decisions that affect individuals and societies, developers carry a significant responsibility. This book highlights the importance of fairness, transparency, and data privacy, encouraging readers to build systems that are not only powerful but also trustworthy and accountable.
This work is intended for software developers, data engineers, and technology enthusiasts who wish to expand their skills into the domain of applied AI. Whether you are beginning your journey or looking to strengthen your expertise, the content is designed to be accessible while still offering depth. Real-world examples and case-based discussions are included to make concepts more relatable and easier to apply.
The field of AI is evolving rapidly, and no single book can capture its entirety. However, the aim here is to provide a strong foundation and a practical framework that will remain relevant as technologies continue to advance. Readers are encouraged to explore further, experiment continuously, and adapt to new developments in this dynamic landscape.
Ultimately, this book is about empowering developers to move beyond using AI as a tool and toward engineering intelligent systems that create meaningful impact. The future of software lies in intelligent, adaptive, and scalable applications-and the ability to build them will define the next generation of innovation.

About the Authors

Vandana Chaturvedi is a seasoned technology leader and Vice President of Software Engineering at JPMorgan Chase, with over a decade of experience driving innovation in enterprise systems and cloud-native architectures. She serves as an Application Owner for critical platforms and is also a Global IP Ambassador at JPMorgan Chase, contributing to enterprise innovation strategy and fostering a strong culture of intellectual property and invention. She is currently pursuing a Master’s degree in Artificial Intelligence from The University of Texas at Austin, complementing her industry expertise with advanced work in deep learning, healthcare AI, and ethical AI systems. She is a member of Phi Kappa Phi, recognizing academic excellence and membership within the top 7.5% of graduate students. Vandana is actively engaged in research, with publications submitted to IEEE and Springer Nature, along with ongoing patent contributions. She is also a multiple global hackathon winner and has served as a judge for corporate and academic hackathons, evaluating innovation, technical depth, and real-world impact.
She is passionate about mentoring emerging talent and advancing responsible, scalable, and impactful AI-driven solutions.

Raviteja Narra is a Data Engineer with over five years of experience building scalable data pipelines, optimizing ETL workflows, and delivering data-driven solutions across cloud platforms. He is currently working with a financial services organization in the United States, where he focuses on Snowflake and AWS-based architectures, developing ML-ready datasets, automating data processes, and improving data quality for advanced analytics.
He has previously worked with multinational organizations and consulting firms, gaining hands-on experience with big data technologies such as Apache Spark, Hadoop, and Airflow. His technical expertise includes Python, SQL, cloud services, and modern data engineering frameworks, enabling him to design efficient and reliable data systems.

Raviteja holds a Master’s degree in Computer Science from Kennesaw State University and a Bachelor’s degree in Electronics and Communication Engineering from VIT University. He is passionate about leveraging data to support better decision-making and scalable data solutions."
"Satyadhar Kumar Chintagunta is a skilled software engineer and data-driven technology professional with a strong academic and industry background. He holds a Master of Science in Computer Science from the University of North Carolina at Charlotte, where he graduated with an outstanding academic record and specialized in areas such as big data analytics, software system design, and database systems. Currently working as a Software Development Engineer at Amazon, he focuses on building scalable, cloud-native applications using modern technologies including Java, TypeScript, and AWS. His professional journey also includes experience in data engineering, backend development, and analytics, where he has contributed to optimizing data pipelines, enhancing system performance, and developing intelligent data solutions.
With expertise spanning full-stack development, cloud computing, and DevOps practices, Satyadhar is passionate about leveraging technology to solve complex real-world problems. He continues to explore innovative solutions at the intersection of software engineering and data science, aiming to create impactful and efficient digital systems.

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