Bondu Chandu
Machine Learning Engineer • Hyderabad, Telangana, India • c*****************@gmail.com • +91*******146 • drivetube.ai/•••••
Professional Summary
Machine Learning Engineer with 0 years of experience pursuing M.Tech in Mathematics and Computing. Experienced in building distributed ML pipelines, multivariate time-series forecasting, and communication-efficient distributed training for large transactional and telemetry datasets. Skilled in PyTorch-based model development, feature engineering, model stabilization techniques (AMP, gradient clipping, LR scheduling), and deploying inference endpoints with FastAPI and Docker.
Technical Skills
Programming Languages: Python,C,C++,R
Frameworks and Libraries: NumPy,Pandas,Matplotlib,Seaborn,Scikit-learn,PyTorch,Neural Networks,LSTM,Reinforcement Learning,FastAPI
Databases: SQL,PostgreSQL,MongoDB
Cloud and DevOps: Docker,Linux
Data and Analytics: XGBoost,LightGBM,Feature Engineering,Hyperparameter Tuning
Tools and Methodologies: Git
Distributed Training & Optimization: LocalSGD,Distributed Training,Automatic Mixed Precision,Gradient Clipping,Learning Rate Scheduling,Cosine Annealing
Problem Domains: Time-Series Forecasting,Anomaly,Fraud Detection,Simulation & Stochastic Optimization
Work Experience
ISRO SDSC SHAR
Sriharikota / Hyderabad, India
Machine Learning Intern
Feb 2024 – May 2024
Research internship at an Indian space research center working on atmospheric telemetry and launch-site weather forecasting using time-series ML models.
Tech Stack: Python, PyTorch, Pandas, NumPy, Git, Linux
- Built an end-to-end LSTM forecasting pipeline for multivariate atmospheric time-series (420K+ observations across 7 variables) including reusable preprocessing, feature engineering, and sequence-generation workflows using Python, Pandas and NumPy.
- Implemented circular encoding for wind-direction to preserve angular continuity; integrated the encoding into the pipeline and evaluated forecasting impact using R², RMSE and MAE across multiple model configurations with a 70/20/10 train/validation/test split.
- Performed hyperparameter tuning and training-stabilization techniques (gradient clipping, LR scheduling, AMP) during LSTM training to improve convergence and robustness; achieved R² up to 0.9998 and sub-unit RMSE on most continuous variables.
- Designed multi-horizon sequence generation and efficient batching to enable GPU-accelerated training on large time-series datasets and to support multi-step forecasting experiments.
- Established a consistent model evaluation regime to compare configurations across variables and report operational metrics for forecasting decisions used by domain stakeholders.
- Packaged and documented reproducible experiment code and model artifacts on GitHub to support future deployment and research handoff.
Projects
Distributed Fraud Detection for Payment Systems using LocalSGD
Tools Used: Python, PyTorch, LocalSGD, FastAPI, Docker, AMP
- Engineered a communication-efficient distributed fraud detection framework using PyTorch LocalSGD across 5 simulated worker nodes with non-IID data partitioning to mimic geographically distributed payment data centers.
- Trained and evaluated models on a 6.36M-transaction dataset, achieving 99.94% accuracy and 0.9906 ROC-AUC with a 0.680 F1-score on the minority fraud class under realistic class imbalance.
- Integrated Automatic Mixed Precision, gradient clipping, and learning-rate scheduling to stabilize distributed training and reduce time-to-convergence.
- Deployed real-time fraud inference via FastAPI inside Docker containers and built monitoring scripts and network simulations to evaluate distributed training behavior under network variability.
RapidoSim — Ride-Hailing Simulation
Tools Used: Python, Reinforcement Learning, Stochastic Optimization
- Developed a multi-agent ride-hailing simulation modeling supply-demand dynamics as a Markov Decision Process with dynamic pricing as the action space.
- Applied reinforcement learning and stochastic optimization to benchmark adaptive pricing policies against static and surge-multiplier baselines within a modular experimentation framework.
Thermal Throttling Prediction via System Telemetry
Tools Used: Python, XGBoost, Scikit-learn, Feature Engineering
- Built an ML pipeline to predict CPU thermal throttling from hardware telemetry, engineering temporal features to capture trends and seasonality.
- Evaluated XGBoost models for early throttling detection, achieving R² ≈ 0.355 and MAE ≈ 0.73°C to support proactive thermal management decisions.
Convex Optimization Essentials for Linear Models
Tools Used: Python, NumPy, PyTorch, Matplotlib
- Implemented Gradient Descent, SGD, Mini-Batch GD, and SAGA from scratch and benchmarked optimization algorithms to study convergence properties.
- Validated optimizer performance with cross-validation experiments, achieving 96.5% cross-validation accuracy and visualized convergence behavior across training settings.
Education
Dr. B.R. Ambedkar National Institute of Technology, Jalandhar
M.Tech, Mathematics and Computing • Jalandhar, India • 2025 – 2027
JNTUA College of Engineering, Kalikiri
B.Tech, Computer Science and Engineering • Kalikiri, India • 2020 – 2024
Powered by Drivetube · Create your own profile at drivetube.ai
Explore Drivetube
- Drivetube Profile — your free digital resume — at drivetube.ai/in/your-name: one true standard resume with a Hiring Snapshot (visa status, expected salary, notice period, work preference, relocation), an ATS-ready PDF download and a single shareable link. Free forever; interview requests come from verified employers and your contact details stay masked until you accept. Documentation.
- Free Job Board — verified openings crawled ATS-by-ATS from 100,000+ real company career pages across 35 ATS platforms. Shows the true posting date from the source ATS — not when a listing was indexed — and deletes every general listing 3 days after it was actually posted. No ghost jobs, no ad-sponsored listings, no staffing reposts, no account needed. Documentation.
- Job Hunt Program — managed job hunting, a one-time purchase from $199.99. JobScout matches verified roles to your real experience band, Blend AI writes a uniquely tailored resume and cover letter for every application, and the Autofill extension fills the form — or Let Us Apply submits it for you. Documentation.
- Resume Writing Services — human-written, ATS-optimised resumes by senior career writers, from ₹499.99 / $25.99. Available in every country, written to the destination country's own standard — a US resume, UK CV, German Lebenslauf and Indian resume are genuinely different documents. A paid service, separate from the free Drivetube Profile. Documentation.
- Community Membership — from $4.99/month (₹1,999/year in India). Unlocks the gated job-board filters — visa sponsorship, security clearance, workplace and application time — plus Job-Scout AI matching, Resume Report AI, Interview AI prep sheets, Recruiter Outreach AI sent from your own Gmail, Apply or Skip triage, a daily market feed and a $10,000+ library including 23 ATS-validated resume templates. Documentation.
- Drivetube Hire — for employers — hiring with no job postings and no applications. Paste your real job description and AI matches it against candidates' true standard resumes, returning ranked candidates with a match %, matched and missing skills and written reasoning. Free tier included; employers pay, candidates never do. Documentation.
Full product documentation — every product explained, with feature-by-feature comparisons against the job boards, AI apply tools, resume services and hiring platforms people actually use.
The job board covers the United States, India, United Kingdom, Canada, Europe and Australia, and Resume Writing Services are available in every country.