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Software Engineer with 1+ years in AI/ML, Backend Development & DevOps
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Highly motivated Software Engineer with 1.3 years of experience in AI/ML, backend development, and DevOps. Proficient in Python, PyTorch, TensorFlow, and various backend frameworks like FastAPI and Flask. Demonstrated ability to design and deploy AI-driven solutions, automate compliance processes, and optimize backend services for improved performance and scalability. Eager to leverage strong technical skills and problem-solving abilities in challenging software engineering roles.
Lovely Professional University
B.Tech. (CSE) · Computer Science Engineering
August 1, 2022 – June 30, 2026
SISA Information Security
AI Research and Development Intern
September 1, 2025 – Present
Bengaluru, Karnataka, India
ITJOBXS
Backend Development Intern
February 1, 2025 – August 31, 2025
India
On-Device Multi-Agent System for Behavior-Based Anomaly and Fraud Detection
January 1, 2024 – June 30, 2026
Designed a multi-agent fraud detection framework integrating Autoencoders, CNNs, LSTMs, Isolation Forest, and One-Class SVM. Developed feature engineering pipelines for app-usage and movement data to improve anomaly detection accuracy. Optimized models for on-device deployment using TensorFlow Lite quantization and latency evaluation. Implemented real-time inference on edge devices ensuring low-latency fraud detection. Built a Streamlit dashboard and Kivy mobile demo application for visualization, monitoring, and explainability.
View ProjectVision Enhanced Waste Sorting and Recycling in Urban Areas
January 1, 2024 – June 30, 2026
Developed a CNN-based waste classification system using 4-channel image input (RGB + Edge Map) for waste identification. Implemented image preprocessing using Canny edge detection, contrast enhancement, and Gaussian blur. Built a custom Keras data generator for efficient batch processing and scalable training on large datasets. Achieved high accuracy in automated waste categorization for smart urban recycling systems.
View ProjectEduRAG - Multimodal Educational Retrieval-Augmented Generation System
January 1, 2024 – June 30, 2026
Developed a multimodal Retrieval-Augmented Generation (RAG) system for adaptive educational content delivery. Implemented ChromaDB vector database with SentenceTransformer (all-MiniLM-L6-v2) embeddings for semantic retrieval. Designed personalized learning paths based on student level, progress tracking, and learning style adaptation. Evaluated performance using retrieval success rate, relevance scores, and RAGAS metrics.
View ProjectCultural Fit Analysis
The candidate's academic projects and internship experience show a strong inclination towards cutting-edge AI/ML technologies and their practical applications, aligning well with an innovative and technically driven culture. The diversity of projects, from fraud detection to educational RAG and waste sorting, indicates a broad interest and adaptability. The current internship in AI Governance and GRC suggests an understanding of responsible AI development, which is a valuable cultural asset. The candidate's focus on MLOps and deployment workflows also indicates a pragmatic, delivery-oriented mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations (e.g., multi-agent fraud detection, multimodal RAG). The internship at SISA Information Security highlights an ability to work on critical, compliance-driven projects, suggesting a detail-oriented and responsible approach. The project descriptions indicate an understanding of end-to-end system development, from model design to deployment and monitoring. While direct evidence of teamwork or stress handling is not explicitly provided, the scope of projects implies collaborative potential and ability to manage technical challenges.