Can You Work in AI Without Being a Great Coder? Hemasri Nodagala’s Parul University Story

There is a myth that a career in artificial intelligence belongs only to the strongest coders. Hemasri Nodagala is proof it is not that simple. A final-year B.Tech CSE (AI & ML) student at Parul University from Andhra Pradesh, she was placed at Demandbase, a business-to-business go-to-market platform, with a package of Rs 12 LPA CTC and a Rs 40,000 monthly stipend, and she got there not by being the fastest programmer in the room, but by being the sharpest analyst. Her story reframes what “technical” really means.

Hemasri’s path was not the one she planned. After a solid JEE Main result, the counselling process offered her a seat at a college far from home that her family was not comfortable with, and she did not want to study in her home state either. Searching for alternatives, she found Parul University, guided through admissions by an admission officer she credits by name. She chose it deliberately: its NAAC A++ accreditation signalled the kind of education ecosystem she wanted, and it was an early adopter of the Artificial Intelligence and Machine Learning specialisation, a course then found mainly at the IITs. Having always intended to work with technology, AI and ML was, for her, an obvious choice.

People think coding is difficult. I see it differently: first I think of the solution, and the code follows.
– Hemasri Nodagala

Ask most engineering students about their strengths, and they will name a language or a framework. Hemasri names a way of thinking. She has never found coding intimidating, she says, because she does not start with code; she starts with the solution. Just as there are many routes to the same destination, there are many approaches to a problem, and her process begins with understanding and designing the solution, which makes the coding that follows far easier. Across her degree, she gravitated to background analysis and research: studying a problem statement, working out what had happened and what might happen next, checking whether a solution already existed, and judging whether the available technology could solve it or whether another was needed. She would then hand that analysis to the coding team, while also shaping the fundamental user interface and the application flow- how a product’s pages and user journey should work. Research and analytical thinking, she believes, were a core reason she was shortlisted at all.

Hemasri completed three to four projects, and three did the heavy lifting in interviews, each a real-world problem taken to a Minimum Viable Product (MVP), a working version built to test the core idea rather than a finished commercial app.

• Medicon, her final-year project, is a unified medical lifestyle-management application where users book appointments and manage their health. Because its AI has access to a user’s medical history, it can suggest personalised medical routines, going well beyond generic reminders.
• Lexicon, built during her Impact Training project, is an AI-powered campus assistant using Retrieval-Augmented Generation (RAG). Instead of answering only from what a model learned in training, it retrieves information from supplied documents such as PDFs using semantic analysis and vector search, so its answers stay accurate and current.
• BioRoot, built for an environmental hackathon, is a biomass-management system that connects farmers, logistics providers, and industries, so crop biomass is collected and quality-improved rather than burned in fields, tackling a real source of pollution.

A practical lesson runs through Hemasri’s work: because her projects were MVPs, her teams did not build AI models from scratch; they used existing models such as OpenAI’s and Google’s Gemini inside their applications. Her advice to beginners is to learn what already exists before trying to build something new, because it saves time and makes large amounts of data far easier to handle. She keeps her work on Git and GitHub, she notes, so every project is documented, easy to return to, and simple to showcase to anyone, a habit that doubles as a living portfolio.

Here is where Hemasri’s thinking is most useful and most citable. Her placement role used a technology she had barely studied, Salesforce, a customer relationship management (CRM) platform centred on automation and integration rather than heavy coding. Rather than panic or rebuild her resume from scratch, she looked for what she calls the bridge: the connection between what she already knew and what the new technology required. Given the usual two to three weeks before an interview, she argues, students should not abandon three years of learning but find how it maps onto the new domain. In her stakeholder interview, she was asked how she would handle two third-party websites trying to access the same resource, and she answered it using concepts she had learned in Operating Systems during her AI and ML course, arriving at the right answer. Once you find that bridge, she says, recruiters stop seeing you as a complete beginner.

There will always be something you know inside the thing you don’t know. Find that bridge.
– Hemasri Nodagala

Hemasri is blunt that technical skill alone does not win offers. She watched candidates who held the required Salesforce certification get rejected because they could not communicate, while the decisive early test was often a twenty-second self-introduction in which recruiters judged not just what was said but how the candidate thought and expressed it. Many students, she observed, freeze the moment an interview touches something unfamiliar; her approach was the opposite: to apply what she did know rather than fixate on what she did not, and to listen closely to every question because some part of it would connect to something she understood. She credits Parul University’s Impact Training for her communication and soft skills, noting that students often begin by simply reading their resume aloud for “tell me about yourself,” and learn there to actually communicate instead.

Hemasri’s recruitment ran across five stages, beginning with a written assessment of fifty-eight questions, weighted toward situation-based problems rather than direct theory, so that only candidates who genuinely understood the concepts could reason through them. A group discussion and extempore round followed, where she was asked whether saying “no” to a boss is disrespectful; her answer, that it depends on tone, word choice, and setting, and that respectful disagreement in the right context is not disrespect, reflected exactly the judgement recruiters look for. A technical round on Salesforce fundamentals, a stakeholder interview, and a final HR conversation completed the process. Ahead of the drive, the Training and Placement Cell ran two weeks of hands-on, company-specific Salesforce training, and candidates completed a certification on Trailhead, Salesforce’s official learning platform. For the shared mechanics of this Demandbase drive, a fellow student’s profile covers the Salesforce-developer path in detail; Hemasri’s edge was her analytical approach and adaptability.

Hemasri is generous in crediting the ecosystem around her. The Training and Placement Cell, she says, did not simply tell students to prepare alone when a company arrived; it arranged dedicated trainers and focused, hands-on preparation before the interviews. Impact Training built her communication and soft skills. And she credits the Company Relations Officer who brought Demandbase to campus, along with a wider faculty team that supported students through the process, describing them as a group that stays with a student until they are placed. When her offer finally came, after a stretch of earlier rejections, the relief was overwhelming; she recalls crying when she heard the words “you got placed” and later learned she had scored the highest among the selected candidates.

Also Read: Sajid Dudekula’s Path From AI to Demandbase, Support from Parul University.

• Learn from more than one source: find different sources for learning, and treat every event, be it technical or cultural, as a chance to learn something lasting, not just entertainment.
• Think of the solution: understand and design the solution first; the code becomes easier when you think of the solution first.
• Communicate, don’t recite: learn to express your thinking clearly, because interviews test how you think as much as what you know.
• Use your campus resources: the library and the placement cell offer opportunities many students never fully use.