Phani Venkata Kumar Koduri
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Machine Learning & Computer Vision
Mechanical Engineering Graduate with a self-directed focus on applied AI, building pipelines in computer vision, predictive analytics, and deep learning. Strong mathematical grounding in linear algebra, probability, and ML theory. Hands-on with PyTorch, TensorFlow, and OpenCV from design through deployment. Drawn to roles where models meet messy real-world data and actually have to work.
Sri Vasavi Engineering College
B.Tech · Mechanical Engineering
August 1, 2022 – June 30, 2026
Aditya Junior College
Intermediate · MPC
June 1, 2020 – May 31, 2022
VDS ZP High School
Matriculation
N/A – Present
ASM Technologies Ltd.
Junior Project Manager
October 1, 2025 – April 1, 2026
Bengaluru, Karnataka, India
CSIR - National Metallurgical Laboratory
Summer Research Intern
May 1, 2025 – July 1, 2025
Jamshedpur, Jharkhand, India
Automated Object Detection & Cropping System
July 1, 2025 – September 1, 2025
Built a YOLOv8 + OpenCV inference pipeline to detect and crop objects from 80+ COCO classes, auto-organising outputs into class-labelled folders – cutting manual dataset curation from hours to minutes. Shipped both a GUI (non-technical users) and a CLI (batch automation), making the tool production-adaptable across research and engineering workflows.
Predictive Maintenance Pipeline NASA IMS Bearing Data
May 1, 2025 – June 1, 2025
Engineered a full ML pipeline on NASA IMS vibration data: FFT spectral feature extraction → fault classification (Random Forest) → anomaly detection (LSTM Autoencoder). Integrated a real-time alert system via Python smtplib, emailing notifications when bearing anomaly scores crossed learned thresholds. Applied statistical signal processing (RMS, spectral kurtosis) across full bearing lifecycle data, achieving consistent early fault detection ahead of failure events.
Summer School in Machine Learning
Amazon
January 1, 2025 – Present
Getting Started with Deep Learning
NVIDIA
January 1, 2025 – Present
Deep Learning
IIT Madras - NPTEL
January 1, 2025 – Present
ML for Engineering & Science Applications
IIT Madras - NPTEL
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects show a strong initiative in applying AI/ML to real-world problems (object detection, predictive maintenance). Their internship at CSIR-NML, though not directly AI-focused, demonstrates a research-oriented mindset and problem-solving capabilities in a scientific context. The project management experience, while not directly technical, indicates an ability to work in structured teams and manage deliverables. The candidate's profile suggests a self-directed learner drawn to roles where models meet messy real-world data, aligning well with an innovative and practical engineering culture. However, the lack of diverse industry experience beyond academic projects and a short project management stint limits the depth of cultural fit assessment.
Soft Skills & Operational Fit
The candidate demonstrates project management skills from their Junior Project Manager role, including stakeholder management, delivery planning, risk tracking, and team coordination. This indicates an ability to work within structured environments and manage project timelines, which is beneficial for operational fit. The development of both GUI and CLI for a project also suggests an understanding of diverse user needs and production readiness.