How Punarvitha Kickstarted Her AI/Data Engineer Journey as a Fresher

In today’s fast-paced tech landscape, breaking into the world of artificial intelligence can seem daunting especially for fresh graduates. But for Punarvitha, a determined computer science student from Tirupati, ambition, persistence, and the right guidance turned that challenge into a success story. Without any prior industry experience, she carved her path into the competitive AI space and landed a role as a Data Engineer. Let’s dive into how she made it happen.

From Learner to AI Professional: Punarvitha Shares Her Job Hunt Journey

In this exclusive Q&A session, she opens up about her journey from being a curious learner to landing her first role as an AI/Data Engineer. 

Q1: Can you introduce yourself and tell us what sparked your interest in data science?

I’m Punarvitha, and I hail from Tirupati, one of the holy towns in India. I completed my graduation in Computer Science Engineering during my final year, I realized I wanted to pursue a career in data science. The idea of working with data, building models, and solving real-world problems really appealed to me.

Q2: What challenges did you face when starting your data science journey?

Although I came from a programming background, machine learning was quite challenging for me. In college, we were only taught the basics with limited practical exposure. When I joined the course, the detailed explanation of algorithms from how they work to when and why to use them made a huge difference. With support from my mentor, especially during machine learning modules, I gained in-depth knowledge and confidence.

Q3: What projects did you work on during your training and internship?

I worked on multiple projects that gave me hands-on experience. During my internship, I was part of a four-member team led by a mentor named Sharma. One of our projects involved building an ML model to categorize potential customers into high or low potential. We were given a one-month timeline to deliver results, and this project helped sharpen my understanding of supervised learning and team collaboration.

Q4: How was your transition from online learning to doing an internship physically?

Most of my training was online since I was still completing my graduation. But for the internship, I joined physically to get hands-on collaboration and better exposure. It was a good decision because interacting directly with mentors and team members helped me understand professional expectations and communication dynamics.

Q5: What was your biggest learning from working on real-time client projects?

The biggest takeaway was how different real-time projects are from what we do in a controlled learning environment. In our training, datasets are clean, and problems are well-defined. But in actual projects, over 50% of the time goes into understanding and cleaning data. You need to understand the business context, work closely with team members, and manage timelines.

Q6: Is transitioning into a data science job as tough as it seems?

Yes, the transition is challenging but not impossible. It becomes more demanding when you start working on real-time projects. The capstone projects we do during training are good practice, but companies expect candidates to demonstrate practical, real-world experience.

Q7: What role did hands-on projects play in your preparation?

Hands-on projects played a major role in preparing me for job interviews. The moment you deal with real data, work with a team, and interact with clients, you understand how different it is from textbook learning.

Q8: How important is NLP and LLM knowledge in today’s interviews?

Extremely important! Almost every company I interviewed with asked questions related to Natural Language Processing (NLP) and Large Language Models (LLMs).

Q9: How did the Placement Assistance Team (PAT) support your journey?

The PAT team was incredibly supportive. From resume building to mock interviews, they guided me through every step. They also shared job openings regularly based on my location preference Bangalore and Chennai.

Q10: How many interviews did you attend before getting selected?

I attended multiple interviews before landing my job at Data Spark AI Solutions. In one interview, I was asked about LLM models and NLP applications. Another company tested my Python coding and machine learning concepts.

Q11: Is additional study material needed outside the course curriculum?

Not really. The course materials were more than enough. All coding steps and concepts were well-documented and accessible in the student portal. From Python programming to machine learning techniques, everything I needed was available.

Q12: Any final advice for aspiring data scientists?

Yes! Be curious and consistent. Don’t expect instant results. Learning data science or AI can be overwhelming, especially with subjects like machine learning, statistics, or NLP. But if you follow a step-by-step path, ask questions, do projects, and seek mentorship you’ll eventually get there.

Refer these below articles:

• From Electronics Engineer to AI/Data Engineer: Praveen’s Path to Success
• From Career Break to AI/Data Engineer: Kiruthiga’s Inspiring Journey
• From Graduate to AI/Data Engineer : Sachin’s Journey into the AI

Key Takeaways from Punarvitha’s AI/Data Engineer Journey

From overcoming initial challenges in machine learning to working on real-time client projects and acing job interviews, her story is a roadmap for aspiring professionals. Here are the key lessons and insights from her successful transition into an AI/Data Engineer role.

• Early Inspiration: Punarvitha’s interest in data science was sparked during her final year of Computer Science Engineering, driven by a passion for data-driven problem-solving.
• Initial Challenges: Despite having a programming background, machine learning was initially tough due to limited practical exposure in college.
• Mentorship Made the Difference: Structured mentorship, especially during machine learning modules, helped her gain confidence and deep understanding.
• Hands-On Experience with Projects: Real-world projects, like categorizing customer potential, enhanced her skills in supervised learning, data handling, and teamwork.
• Value of Physical Internship: Transitioning from online learning to an in-person internship helped her better understand professional communication and collaboration.

Read these below articles:

• Rising Demand for Artificial Intelligence in Gandhinagar
• Why Artificial Intelligence is in High Demand in Ludhiana
• Rising Demand for Artificial Intelligence in Amritsar

Punarvitha’s journey proves that with determination, structured learning, and hands-on experience, freshers can successfully break into the AI field busting the myth that it's only for seasoned professionals. With the right course and placement support, a career in data science is achievable. The wearable AI market is booming, projected to grow from $26.8 billion in 2023 to $166.4 billion by 2030 (Grand View Research), highlighting the vast opportunities ahead.

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DataMites Artificial Intelligence Institute in Hyderabad offers a comprehensive AI course with hands-on projects, expert mentorship, and strong placement support perfect for both freshers and professionals ready to upskill in the city’s thriving tech scene.

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