From a Confused First Year to HCL and IBM Offers: How Naga Chandrika Eluru Built Her B.Tech CSE Placement at Parul University

Not every placement story begins with a head start. Naga Chandrika Eluru‘s begins with something rarer: radical honesty. She was a backbencher in school. She started improving in middle school. She arrived at the B.Tech in Computer Science Engineering at Parul University not with a clear technical plan, but with a vague interest in computers shaped by the influence of her brother, an engineer who kept telling her about what the technology industry actually looked like from the inside.

She did not code in her first year. Her serious engagement with programming began only in her second year, with Python as the entry point after Java proved difficult. Today, the same student has received a full-time offer from HCL Technologies as an Autonomics Engineer at ₹4.5 LPA, along with a Letter of Intent from IBM for the Associate Software Engineer role. She completed a four-month industry internship at Keyanna Technology Pvt Ltd as a Python Developer working on a live AIOps data pipeline project. She maintained an 8.35 CGPA across eight semesters. None of it arrived easily, and she is the first to acknowledge it.

The concept that was covered in Impact Training was enough for the placements.

Naga Chandrika Eluru, B.Tech CSE, Parul Institute of Engineering and Technology, Batch 2026

Chandrika is from Andhra Pradesh and completed her 12th with 90.3 percent in PCM at Sri Sai Sidhartha Junior College, Ananthapur. Her decision to pursue Computer Science Engineering was not purely self-driven. She credits the influence directly to her elder brother, an engineer, who consistently showed her what the technology industry looked like from the inside: the career paths available, the skills the industry was building on, the trajectory accessible to someone willing to invest in learning the right things.

It might be influence. It’s not my own interest. I got interested due to the influence. He is an engineer himself. He is very good in this profession. He keeps pushing me. There are several opportunities. He keeps on telling me about the technologies.

Naga Chandrika Eluru

Her decision to join Parul University was grounded in evidence rather than aspiration. Seniors from her school and locality had enrolled at Parul and had returned with verifiable placements. The teaching, by their account, was good. The placement outcomes were real. For Chandrika, who was making a practical decision about where to spend four years, that direct reference from people she trusted carried more weight than any institutional brochure could have. Parul University’s NAAC A++ accreditation at 3.55 CGPA, Category 1 University status, and the documented placement track record provided the verification she was looking for. She joined, and she explored.

Most placement success narratives skip the first year or gloss over it. Chandrika does not. She names it plainly. She did not start coding in her first year. She was confused about what to do and how to begin. She studied for semester examinations and believed, as many students do early on, that examination scores alone would create the opportunities she was looking for.

I was literally confused what to do so I just stopped. But I used to say to my brother that I am doing it. I used to learn more for semesters instead of these technologies.

Naga Chandrika Eluru

It was only in her second year that she seriously began exploring programming. She started with Python, the language that finally made sense to her after Java had proved difficult. Faculty support reinforced the early discipline. Weekly sets of ten to fifteen Python problems, assigned and worked through in structured classroom time, gave her a controlled environment in which her growing confidence in the language could compound.

She began solving problems on LeetCode, not on a structured plan at first, but developing the habit of sitting with a problem until something moved. She noticed quickly that her solutions, while correct, were not optimal. They worked. They lacked the framework of time complexity analysis and algorithmic approach selection that distinguishes a placement-ready problem solver from a casual one. That gap between working and optimal was the gap that the structured discipline of her third and fourth years would close.

Impact Training at Parul University is the placement preparation programme run by the Training and Placement Cell. It is a structured, intensive initiative that takes students through the full range of technical and professional skills required to compete in real placement drives. For Chandrika, it was the inflection point between random effort and directed preparation. The full programme account is in the dedicated article on Parul’s Impact Training and the Striver DSA path.

During her third year, she learned of Striver’s A2Z DSA Sheet, a structured roadmap for Data Structures and Algorithms that organises topics in a deliberate sequence rather than offering a flat list of mixed problems. By using the sheet alongside the structured sessions in Impact Training, she worked through approximately 200 problems covering arrays, strings, linked lists, stacks, queues, and trees. The number of problems was less important than the structure. The structure produced a clear understanding of time complexity, how to recognise the optimal solution pattern for a given problem class, and how to develop a methodical approach when first encountering a new problem.

The training also operated as mentorship. The instructor guided students not only on technical content but on what came next: what skills to build after B.Tech, which technologies the industry was actively hiring for, and which learning platforms outside the classroom were worth student time. The combination of structured DSA work, Impact Training’s coverage of operating systems, DBMS, and OOP concepts, and ongoing career mentorship gave Chandrika a coherent preparation foundation rather than a scattered one.

Chandrika’s portfolio is the verifiable artefact behind the interview-room confidence. Her public GitHub profile shows 477 contributions across 20 repositories in the past year, the output of someone who shows up consistently rather than working in bursts. Her projects span web development and applied AI.

• Perplexity-style AI search assistant: built with Next.js, Supabase, and the Brave Search API, delivering improved search rendering and retrieval efficiency.
• StoryMedia: full-stack storytelling web application built in a 36-hour hackathon, integrating the OpenAI API for AI-generated story creation and gTTS for audio conversion across five or more languages.
• Argo-XIT: academic full-stack project using React.js and Flask, featuring a scikit-learn crop prediction model with over 92 percent accuracy, deployed on Render’s PaaS with a CI/CD pipeline.
• Agriculture Assistant (final-year project): integrates three datasets covering disease-to-treatment mapping, plant image to disease identification, and weather, location, and soil-type to crop recommendation. A single tool a farmer could use to decide what to plant, identify what is wrong with their crop, and find the right remedy.

The final-year Agriculture Assistant project carries the clearest signal of how Chandrika thinks about what technology is actually for. The challenge that hit hardest during the build was not technical. It was the gap between what the tool could do and who could actually use it. Many small farmers in India do not have the digital exposure that the application’s user interface assumes. The technical capability of the application became secondary to its practical reach. The question of accessibility, in her account, became the central design constraint rather than a peripheral feature.

Chandrika’s industry internship at Keyanna Technology Pvt Ltd ran from January to April 2026 as an on-site Python Developer role. The first month was orientation into the company’s AI and object-oriented programming architecture. The second month, she was assigned to a live AIOps data pipeline project designed to normalise and consolidate data from two existing company applications. The architecture was built across multiple microservices, each handling a different stage of data movement. Her work was assigned to the destination microservice, the component responsible for receiving and storing processed data into the format required by the company’s machine learning models.

The biggest technical challenge came not from the logic of her code but from the gap between local development and server deployment. Code that ran perfectly on her local machine produced multiple server-side errors when pushed to production. The errors were not in her business logic. They were in the surrounding ecosystem: Docker configuration, server-side dependencies, and the operational layer that production software actually runs on. The internship taught her, in her account, that professional software development requires understanding the ecosystem around the code, not just the code itself. The internship also ran simultaneously with her final-year project, a logistical challenge she managed with the help of her project teammates.

Read More: Parul University’s Placement Cell Behind a 45 LPA offer.

Chandrika’s placement drives produced two outcomes within the same window: a full-time offer from HCL Technologies as an Autonomics Engineer at ₹4.5 LPA, and a Letter of Intent from IBM for the Associate Software Engineer role. The complete process documentation, including the four-stage HCL drive, the English-test logistics, and the HCL-versus-IBM decision logic, is in the dedicated HCL placement deep-dive. The headline outcome is that Chandrika is currently undergoing pre-joining training with HCL Technologies, further strengthening her fundamentals ahead of the formal joining date.

Her choice of HCL over IBM was not about prestige or package. It was about role clarity. The HCL role was defined upfront: Autonomics Engineer, with a known skill set and a known engineering domain. The IBM Associate Software Engineer role assigned the actual technology stack after joining, which created uncertainty about what she would be working on day-to-day. For a candidate evaluating two offers, role definition before joining is a structurally rational basis for choosing.

These are not my dream companies. I will go through HCL for one to two years, learn the skills required for an AI DevOps developer, and then take those companies.

Naga Chandrika Eluru, on her five-year plan

The five-year plan she articulated is the same kind of structured thinking that produced the placement itself. HCL and IBM are starting points, not destinations. She intends to spend one to two years building specialised AI DevOps skills at HCL, and then to use that foundation to pursue more advanced roles at organisations operating in her target technical area. The clarity of the plan is, in itself, a measure of how far the student who entered her first year confused about what to do has travelled.

Chandrika’s CGPA of 8.35 across her B.Tech is not a number she earned without deliberate trade-off. Her first four semesters, focused primarily on academic performance, produced 8.8 to 8.9 per semester. When her fifth semester arrived, bringing with it the technical demands of the placement window, her attention shifted toward skill development. The CGPA dropped to 8.0 to 8.1 across the fifth, sixth, and seventh semesters as she chose to invest her hours in DSA discipline, project building, and impact training preparation rather than additional academic margin.

She did not pretend the trade-off was costless. She named it. An 8.35 CGPA reflects neither a student who sacrificed academic performance to chase placements nor one who chased CGPA at the expense of employability. It reflects a student who consciously balanced both demands, traded off measurably between them, and emerged with classroom credibility and verifiable practical skills.

When asked to apportion her placement success across the factors that contributed to it, Chandrika’s answer was measured. She gave thirty percent to her family, twenty percent to her faculty, thirty percent to Parul University as an institution, and twenty percent to herself. The faculty she singles out are the professors who taught her core computer science subjects, particularly DBMS, operating systems, and the foundational papers that Impact Training reinforced and that the HCL and IBM interviews directly tested.

There are some faculties who helped me really well for core subjects. Just because of them I am this confident about all the core subjects. It really helped me a lot for placements. Compared to several other colleges, my college gave me a lot of placements: compared to friends who are not at Parul.

Naga Chandrika Eluru

The last observation is the one that carries institutional weight. Chandrika arrived at Parul University because seniors from her area had received good placements. She is now, for the next batch of CSE aspirants from Andhra Pradesh looking at engineering colleges, one of those seniors. The continuity of the reference network is the underlying signal that the Tanish Patel Microsoft placement at ₹60 LPA documented separately also confirms: the placement outcomes are real, they are verifiable, and they recur across student profiles rather than appearing in isolated cases.

When asked directly what she would say to a second-year CSE student at Parul University right now who started late, who is confused about DSA, has no internship, and is watching their peers seem further ahead, Chandrika’s answer was short, specific, and unadorned. Start DSA as soon as possible. Use a structured path, like Striver’s A2Z, that has a defined sequence. Stop doing random problems. Start immediately. The advice carries weight precisely because she is the student who followed exactly that recommendation and is now holding two full-time offers from established technology companies.

Chandrika’s LinkedIn profile and GitHub repository are publicly accessible, which is itself part of the verification this placement story rests on. Prospective B.Tech CSE candidates and parents evaluating Parul University as a placement destination can confirm the profile, examine the project portfolio, and read the placement evidence without relying on institutional claims alone.