In the ever emerging technological advancement and for the fact that the technological world is expanding at a very fast pace, the position of a Technical Lead also known as Tech Lead is equally as complex in the way that they weave the three dimensions of technology as stated by (1, 2) management and leadership in one intricate manner as stated by (3, 4). In addition to starting the software creation process (5), Tech Leads are also faced with organizational challenges constituent of dealing with others (6, 7). This dual role means that they have to code in order to maintain their integrity while at the same time supervise plans (8), convene with other stakeholders, and manage training (9). These challenges are even magnified by what is referred to as agile methodologies where a lot of changes are needed due to other emerging needs and also managing of other many cycles of development (3, 10). First of all, the most efficient Tech Leads manage to set and achieve technical goals connected with the development of IT systems and organizational goals, which are inalienable parts of IT systems development (11), such as code optimization and architectural decision-making (12), the organization of the working schedule and distribution of the resources, which do influence a team’s success and their integration into the project (7, 11).

In conclusion, the work of Technical Lead requires not only technical skills and expertise but also the management skills. Albeit, such dynamics can illustrate how effectively or otherwise one is able to balance these two roles may determine the output in a team or a particular project or even the wellbeing of an employee. The usual challenges are as follows: time, work and stress which have solutions in form of planning, assigning tasks and staff trainings. This is helpful in comprehending and defining the advice and issues for the current and potential Tech Leads with varying levels of expertise in their organizational industry as the technology continues to change.

1.Nuthakki, S., Kulkarni, C. S., Kathiriya, S., & Nuthakki, Y. (2024). Artificial Intelligence Applications in Natural Gas Industry: A Literature Review. In International Journal of Engineering and Advanced Technology (Vol. 13, Issue 3, pp. 64–70). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication – BEIESP. https://doi.org/10.35940/ijeat.c4383.13030224. 2.Gichoya JW, Nuthakki S, Maity PG, Purkayastha S. Phronesis of AI in radiology: Superhuman meets natural stupidity [Internet]. arXiv.org. 2018. Available from: https://arxiv.org/abs/1803.11244 3.Nuthakki, S., Bucher, S., & Purkayastha, S. (2019). The development and usability testing of a decision support mobile app for the Essential Care for Every Baby (ECEB) program. In HCI International 2019–Late Breaking Posters: 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings 21 (pp. 259-263). Springer International Publishing. 4.Nuthakki, S, “Exploring the Role of Data Science in Healthcare: From Data Collection to Predictive Modeling”, European Journal of Advances in Engineering and Technology, 2020, 7(11):75-79.  5.S. Nuthakki, S. Neela, J. W. Gichoya, and S. Purkayastha, “Natural language processing of MIMICIII clinical notes for identifying diagnosis and procedures with neural networks,” 2019, [Online]. Available: http://arxiv.org/abs/1912.12397 6.R. de Lemos et al., “Software Engineering for Self-Adaptive Systems: A Second Research Roadmap,” Software Engineering for Self-Adaptive Systems II, pp. 1–32, 2013, doi: https://doi.org/10.1007/978-3-642-35813-5_1. 7.S. Amershi et al., “Software Engineering for Machine Learning: A Case Study,” 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), May 2019, doi: https://doi.org/10.1109/icse-seip.2019.00042. 8.Nuthakki, S., Bhogawar, S., Venugopal, S. M., & Mullankandy, S. “Conversational AI AND LLM’S Current And Future Impacts In Improving And Scaling Health Services,” International Journal of Computer Engineering and Technology (IJCET), Vol. 14, no. 3, pp.149-155, Dec. 2023, https://iaeme.com/Home/issue/IJCET?Volume=14&Issue=3 9.Nuthakki, S., Buddiga, SKP., & Koganti, S. Exploring Deep Learning Models for Image Recognition: A Comparative Review. Signal & Image Processing: An International Journal (SIPIJ) Vol.15, No.3, June 2024 DOI: 10.5121/sipij.2024.15301 10.Nuthakki, S., Kolluru, V. K., Nuthakki, Y., & Koganti, S. “Integrating Predictive Analytics and Computational Statistics for Cardiovascular Health Decision-Making”, International Journal Of Innovative Research And Creative Technology, vol. 9, no. 3, pp. 1-12, May 2023, doi: https://doi.org/10.5281/zenodo.11366389 11.Nuthakki, S., Kumar, S., Kulkarni, C. S., & Nuthakki, Y. (2022). “Role of AI Enabled Smart Meters to Enhance Customer Satisfacti2024on”. International Journal of Computer Science and Mobile Computing, Vol.11 Issue.12, December- 2022, pg. 99-107, doi: https://doi.org/10.47760/ijcsmc.2022.v11i12.010. 12.Sai Kalyana Pranitha Buddiga, Siddhartha Nuthakki, “Enhancing Customer Experience through Personalized Recommendations: A Machine Learning Approach”, International Journal of Science and Research (IJSR), Vol.11, Issue.9, September 2022, pp. 1265-1267, dot: https://dx.doi.org/10.21275/SR24531130722. 13.R. Sarkis-Onofre, F. Catalá-López, E. Aromataris, and C. Lockwood, “How to Properly Use the PRISMA Statement,” Systematic Reviews, vol. 10, no. 1, Apr. 2021, doi: https://doi.org/10.1186/s13643-021-01671-z. 14.H. A. Long, D. P. French, and J. M. Brooks, “Optimising the Value of the Critical Appraisal Skills Programme (CASP) Tool for Quality Appraisal in Qualitative Evidence Synthesis,” Research Methods in Medicine & Health Sciences, vol. 1, no. 1, pp. 31–42, 2020, doi: https://doi.org/10.1177/2632084320947559. P. L. Li, A. J. Ko, and J. Zhu, “What Makes a Great Software Engineer?,” 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 2015, doi: https://doi.org/10.1109/icse.2015.335.

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