Shaik Akram
Professional Summary
Machine Learning Engineer with 0 years of experience building forecasting models and cloud-enabled ML workflows. Experienced in Python-based modeling (XGBoost, LSTM, ARIMA) and model explainability using SHAP, demonstrated through a published demand-forecasting system and interactive Streamlit dashboard. Applied data engineering and ETL practices with pandas and NumPy to prepare time-series data, implemented model versioning, and exported production-ready forecasts. Delivered a full-stack placement portal with React.js, Spring Boot and MySQL that improved engagement metrics. Gained hands-on multi-cloud exposure (AWS, Azure, GCP) during an AICTE cloud internship focused on monitoring and resource optimization. Holds AWS Cloud Practitioner and ServiceNow certifications and is comfortable using GitHub, VS Code and Power BI/Tableau to deliver reproducible, cloud-deployable data products.
Technical Skills
Work Experience
- Monitored application and infrastructure performance across AWS, Azure, and GCP to identify underutilized compute and storage patterns.
- Analyzed infrastructure utilization datasets using Python and pandas to profile CPU, memory and network trends for cloud-hosted services.
- Generated interactive operational dashboards in Power BI and Tableau to surface utilization, SLA trends and anomaly cases for stakeholders.
- Partnered with a team of 4 to right-size cloud resources and propose instance and reservation changes to improve efficiency.
- Implemented alerting thresholds and a tracked incident workflow using GitHub for issue tracking and ServiceNow for ticketing.
- Documented diagnostic runbooks and knowledge-transfer materials to support cloud-ops handover and reproducible incident response.
Projects
- Developed a Streamlit dashboard to deliver automated demand forecasts using XGBoost, ARIMA and LSTM models for e-commerce time-series data.
- Implemented end-to-end ETL with pandas and NumPy to clean, resample and feature-engineer transactional time-series prior to modeling.
- Built automated champion model selection logic and tracked model metadata and versions in MongoDB to support repeatable deployments.
- Implemented SHAP-based explainability and Matplotlib visualizations to surface feature importance and drive actionable inventory decisions.
- Enabled exportable forecasts and performance comparison views to hand off model outputs to downstream analytics and operations teams.
- Designed and implemented a responsive React.js frontend to improve candidate engagement and application workflows.
- Developed a secure backend REST API in Java with Spring Boot and deployed on Tomcat to manage job listings and applications.
- Implemented MySQL schema and optimized SQL queries for listing retrieval and application tracking to support relational data needs.
- Integrated real-time data fetching and state management to provide seamless updates of company details and application status.
- Structured development and releases using Agile sprints with work tracked in GitHub; developed primarily in VS Code.
Education
Certifications
Achievements
- Publication — IEEE SILCON 2025 — 2025: Presented and published 'Predictive Demand Forecasting for E-Commerce Supply Chains' at IEEE SILCON 2025, NIT Nagaland.
- University Hackathon Participant: Participated in multiple university hackathons, demonstrating rapid prototyping and team problem-solving skills.
- Guinness World Record Participant: Participant in 'Most Participants in an Agentic AI Hackathon' organized by Google Developer Groups (GDG).
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