Expanding a Data Engineering Career With AI Expertise
To gain expertise in emerging data technologies and AI, Panchami Baleri, MS’25, information systems, pursued rigorous academics, team projects, and industry networking opportunities as a graduate student at Northeastern’s Silicon Valley campus.
Panchami Baleri, MS’25, information systems, earned a Bachelor of Engineering degree in electronics and telecommunication from the University of Mumbai and worked for three years as a data engineer at Infosys in India. During this time, she developed a strong interest in data systems, automation, and the growing field of machine learning.
As she progressed in her role, Baleri realized she wanted to increase her expertise in emerging data technologies and decided to pursue a master’s degree. Northeastern University stood out to her for its strong emphasis on experiential learning and its specialized focus areas in data engineering and AI. She says courses in data science engineering, large language models, and data architecture closely aligned with her career goals and played an important role in her decision to enroll in the MS in information systems program, which she completed at the Northeastern University at Silicon Valley campus.
As a student, she found the curriculum equipped her with both theoretical foundations and practical skills relevant to the data engineering and AI industry. Courses such as Network Structures and Cloud Computing, Big Data Systems, and Intelligent Analytics taught by Raja Alomari, associate teaching professor, deepened her interest in the cloud and big data systems. The Data Science course taught by Pramod Gupta, adjunct faculty member, enhanced her ability to translate complex data into actionable insights. Baleri also had the opportunity to serve as a teaching assistant for the Data Science Engineering course, which further deepened her understanding of core concepts and allowed her to mentor fellow students.
Baleri says that with the encouragement of Alan Eng, director of strategic partnerships at the San Jose campus, she took on challenges beyond academics. She participated in an experiential learning project where she contributed to building NuGPT, an academic chatbot designed to assist students at Northeastern’s San Jose campus. She worked on designing the data pipeline, data cleaning, and supporting the chatbot’s ability to answer queries using LlamaIndex and the ReAct Agent.
One major challenge the team faced was integrating the backend, frontend, and data pipelines, as the project involved a diverse tech stack. They also initially encountered challenges with data extraction. Despite the complexity, Baleri and her team successfully brought all components together and deployed a working solution.
Looking ahead, she sees strong real-world potential for NuGPT beyond academic use. Its framework can be adapted for enterprise knowledge assistants, HR onboarding, or customer support tools—essentially any environment that requires quick, accurate responses based on internal documentation. She says the experience strengthened both her technical skills and her confidence in applying classroom concepts to real-world challenges.
Baleri participated in several conferences and workshops. She attended the TiEcon conference, gaining knowledge on AI’s transformative impact and participating in a hands-on workshop on Meta’s LLaMA 3 model. She also participated in the GenAI Summit, where she engaged in a hands-on workshop on Corrective RAG using Amazon Bedrock. Additionally, she volunteered at the Google Developer Groups’ DevFest in Silicon Valley at Google’s Sunnyvale campus, where she networked with AI experts and innovators. These events focused on the practical applications of Generative AI and responsible AI systems.
Baleri’s experience at Northeastern has been truly transformative. The blend of rigorous coursework, hands-on projects, and industry exposure at events has solidified her passion for data engineering and AI.
Moving forward, she aspires to work in roles that lie at the intersection of data engineering, AI, and cloud technologies particularly focusing on building scalable data pipelines, developing intelligent systems, and contributing to responsible AI solutions.