Software Engineer with 2+ years in Python, C++, Java & AI/ML
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Software Engineer with M.Tech from NIT Karnataka and 1+ years of industry experience in software development and technical problem-solving. Proficient in Python, C/C++, and Java with a strong foundation in data structures, algorithms, OOP, and SDLC fundamentals. Hands-on experience with ML/AI concepts through academic projects involving computer vision and deep learning. Experienced in root-cause analysis, debugging, and delivering scalable solutions. GATE 98th percentile (2022).
National Institute of Technology Karnataka
M.Tech · Information Technology
August 1, 2022 – June 30, 2024
Dr. A. P. J. Abdul Kalam Technical University, Lucknow
B.Tech · Computer Science Engineering
August 1, 2014 – June 30, 2018
GL Bajaj Institute Of Technology
Data Structures and Algorithms (DSA) Instructor
September 1, 2024 – Present
Greater Noida, Uttar Pradesh, India
Root Innovation Lab Pvt. Ltd. (AIQoD)
Software Developer
September 1, 2021 – June 1, 2022
Pune, Maharashtra, India
Vehicle Number Plate Recognition System
August 1, 2023 – December 1, 2023
Designed an end-to-end ML pipeline for license plate detection using YOLO-v8, SRGAN, and OCR, increasing detection accuracy by 5%. Applied SRGAN for super-resolution image enhancement, improving recognition accuracy to 98% on blurred and low-resolution inputs. Built and validated automated regression pipelines across 200+ images to ensure robust performance under fuzzy and blurred edge cases.
Parallel Implementation of Hybridized Merge-Quicksort
February 1, 2023 – May 1, 2023
Designed and implemented scalable, reusable C++ components for a hybrid Merge-Quicksort algorithm across 2+ distributed machines, focusing on algorithmic optimization and parallel performance. Applied OOP design principles with MPICH to implement MPI communication protocol across distributed nodes using coordinator-worker architecture, improving scalability and fault tolerance. Achieved 25% improvement in sorting performance through systematic algorithmic analysis Quick Sort for smaller datasets, Merge Sort for larger ones.
Cultural Fit Analysis
The candidate's academic projects show a diverse interest in both core computer science (distributed algorithms) and emerging fields (ML/AI). Their experience as a DSA instructor and software developer, combined with competitive programming achievements, suggests a proactive and continuous learning mindset. The breadth of skills and technologies listed indicates adaptability and a willingness to explore different domains, which aligns well with a dynamic engineering culture.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving and analytical skills through their DSA instructor role and algorithmic optimization projects. Their experience in collaborating with stakeholders and applying SDLC best practices indicates a good operational fit for team environments. The ability to consult with students and design curriculum also points to good communication and mentorship potential.