My Experience at Ishitva: A Blend of Challenges and Growth
Pros
Ishitva Robotic Systems Private Limited (IRSPL) was my first job, and it has been a truly transformative experience. I am incredibly grateful to the IRSPL family for their constant support and guidance, always being helpful whenever needed. The company provided an environment that encouraged learning, growth, and multidisciplinary exposure beyond my primary role in AI. While my main focus was Artificial Intelligence, I had the opportunity to collaborate with professionals from mechanical, electrical, and automation engineering, broadening my understanding of interdisciplinary problem-solving. Managing client interactions further enhanced my professional skills and confidence. Ishitva is tackling one of the most pressing challenges—effective waste sorting and recycling. India produces vast amounts of plastic waste, and Ishitva’s mission of “We Sort to Create Value” aligns with the urgent need for sustainable solutions. By leveraging AI technologies like computer vision and machine learning, Ishitva is solving complex waste management challenges that traditional methods cannot address. Their AI-driven solutions enable precise material segregation, driving the transition to a circular economy where waste is transformed into valuable resources. Overall, my time at Ishitva was enriching and fulfilling. The leadership is supportive, the team is passionate, and the work culture fosters continuous learning and innovation. I highly recommend IRSPL to anyone looking to work on impactful real-world problems while developing both technical and interpersonal skills.
Cons
Working at Ishitva presents exciting challenges that push you to grow, but it also comes with its share of demanding moments. Meeting deadlines can sometimes be tough, especially when you're deeply engaged in multiple tasks, requiring effective time management and perseverance. The nature of some of the problems can occasionally feel overwhelming, as the complexity and unpredictability of real-world scenarios in waste recycling demand constant learning and adaptation. Additionally, testing code on-site at the plant can be an essential but time-consuming aspect, ensuring that solutions work seamlessly in real operational environments. However, these challenges ultimately contribute to a dynamic learning experience and provide valuable hands-on exposure to real-world AI applications.
