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AI Engineer with less than a year in AWS Cloud & Machine Learning
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Computer Science postgraduate with strong foundations in Object-Oriented Programming, computer networks, and operating systems. Experienced in building scalable, cloud-native applications using AWS serverless technologies including Lambda, API Gateway, DynamoDB, S3, and Cognito. Developed an AI-Powered CI/CD Pipeline Optimizer using Semantic Q-Learning to reduce test execution time and improve fault detection. Skilled in Python, backend development, and data analysis, with a focus on automation, performance optimization, and real-world problem solving.
PES University
M.Tech · Computer Science and Engineering - Cloud computing
August 1, 2024 – June 30, 2026
GM Institute of Technology
B.E. · Computer Science and Engineering
August 1, 2019 – June 30, 2023
Vishwachetana PU College
Pre-University
June 1, 2017 – May 31, 2019
Mandara Public School
SSLC (Class X)
N/A – May 31, 2017
F13 Technologies
AWS Cloud Intern
December 1, 2025 – February 1, 2026
India
Sumukha Infotech
Software Intern
August 1, 2022 – September 1, 2022
India
AI-Driven Serverless Resume Parsing and Job Matching System on AWS
December 1, 2025 – February 1, 2026
Implemented an automated resume processing pipeline using AWS Lambda, Amazon S3, and AWS Textract to extract and structure candidate data from PDF resumes. Built NLP-driven resume-job matching using similarity scoring and recruiter dashboard, improving match accuracy by 85% and reducing manual screening effort by 70%. Achieved 93-95% text extraction accuracy and ≤ 5 seconds processing time per resume.
AI-Powered CI-CD Pipeline Optimizer for Intelligent Test Case Prioritization
December 1, 2025 – February 1, 2026
Implemented a Hybrid Semantic-Q DRL Agent to prioritize high-risk regression test cases using historical execution data and semantic code-change signals, improving early fault detection (APFD) by 14%. Built an end-to-end training and evaluation pipeline to compute failure probability, risk-impact scores, and APFD, achieving 65-80% failure recall within the top 40% CI execution budget. Ensured 100% dependency-safe execution using DAG-based ordering and demonstrated online adaptation to concept drift, recovering APFD from 0.58 to 0.79 within 10-15 CI cycles.
View ProjectAutomated Leave Request Workflow with Conversational Bot
December 1, 2025 – February 1, 2026
Built a no-code leave approval pipeline where employees submit requests via Telegram bot, triggering automated manager notifications and Google Sheets logging. Reduced leave processing time from manual back-and-forth to under 30 seconds per request with zero human intervention in the routing logic.
Cloud-Based File Storage System with Role-Based Access (AWS Serverless Project)
December 1, 2025 – February 1, 2026
Developed a serverless cloud-based file storage system on AWS enabling secure upload, download, and management of files with role-based access for multiple users. Implemented RBAC using Amazon Cognito and IAM (Admin, Editor, Viewer) and built backend APIs with AWS Lambda and API Gateway, reducing backend infrastructure overhead by ~70% using serverless architecture. Achieved < 2 seconds file upload response time and ~300-500 ms file retrieval latency using Amazon S3 for storage and DynamoDB for metadata management.
Group Photo-Based Attendance Management System
June 1, 2022 – June 1, 2023
Designed and deployed an end-to-end facial recognition pipeline using Python, Flask, OpenCV, Dlib, and HOG, enabling real-time student identification from classroom group photos. Developed a facial embedding model generating 128-dimensional feature vectors, implementing Euclidean distance-based matching (threshold less than 0.5) to ensure high-precision identity verification. Reduced manual attendance effort by 90%, decreasing marking time from 10-15 minutes to under 30 seconds while eliminating proxy attendance risks.
AWS Partner Generative AI Essentials
Unknown
December 1, 2025 – Present
AWS Partner Containers on AWS
Unknown
December 1, 2025 – Present
AWS Partner Migrating Workloads to AWS
Unknown
December 1, 2025 – Present
AWS Partner Accreditation Technical
Unknown
December 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying AI and cloud technologies to solve real-world problems, which aligns well with an innovative and results-driven culture. The diversity of projects, from resume parsing to CI/CD optimization and attendance systems, shows adaptability and a broad technical curiosity. The pursuit of a Master's degree in Cloud Computing further indicates a commitment to continuous learning and specialization. However, the experience level is entry-level, which might require more mentorship in a senior role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to work on complex, multi-faceted systems. The 'Positions of Responsibility' section suggests leadership and organizational skills, which are beneficial for team collaboration and project management. However, without direct interview data, a comprehensive assessment of communication, stress handling, and team collaboration is limited.