Architected an enterprise-grade Agentic AI data analytics platform leveraging Large Language Models (LLMs) for autonomous data analysis and insights generation. Implemented multi-agent reasoning architecture with context-aware processing, enabling natural language data queries and automated visualization generation. Built with TensorFlow deep learning for anomaly detection using autoencoder neural networks, 3D PCA dimensionality reduction , and intelligent parsing supporting multiple file formats (CSV, Excel). Features 8+ interactive chart types with Plotly, domain-specific business insights, and a conversational AI assistant enabling real-time exploratory analysis through natural dialogue. Deployed with Docker containerization and optimized caching for production-grade performance.

Engineered a comprehensive end-to-end data engineering platform for supply chain optimization combining automated data ingestion , SQL transformations with dbt and 8 production-grade ML models for predictive analytics. Implemented XGBoost classification with SHAP explainability to predict late deliveries, Prophet time series forecasting for demand planning, and K-Means clustering for network optimization. Integrated causal inference modeling (OLS) , safety stock optimization formulas, market basket analysis and discrete-event simulation (SimPy) for scenario testing. Architected with Apache Airflow orchestration for automated daily pipelines, PostgreSQL data warehouse with staging and marts layers, and interactive Jupyter analytics notebooks . Deployed with Docker containerization enabling reproducible, production-grade performance across environments.

Engineered a comprehensive data analytics platform to address operational disruptions in automated warehouse shuttle (AKL) logistics at airport facilities. Built a modular Python-based data extraction pipeline using Design Science Research Methodology (DSRM) to parse heterogeneous semi-structured log streams and reconstruct complete, timestamped box journeys across conveyor, lift, and buffer stations. Developed ML-driven failure point localization and delay pattern detection models identifying 3,000+ potential operational delays and surfacing systemic idle wait clusters at conveyor merge zones as critical throughput bottlenecks. The platform delivered real-time operational transparency enabling early intervention workflows and data-driven preventive maintenance strategies.

Designed and deployed end-to-end demand forecasting pipelines on Google Vertex AI orchestrated with Apache Airflow and transformation layers in dbt + BigQuery leveraging feature engineering (seasonality extraction, regional normalization, holiday encoding), data quality validation, and automated model selection across ARIMA / XGBoost / DNN regressors. Produced double-digit MAPE reduction improving regional inventory allocation accuracy, reducing stock imbalance risk, and enabling earlier capacity planning signals.

Led PUMA's cloud modernization by authoring reusable Terraform module library covering VPC design (subnets, routing), IAM least-privilege roles, GCS lifecycle policies, Cloud SQL provisioning, service accounts & workload identity federation. Integrated Git-driven CI/CD pipelines with environment promotion (dev→staging→prod), automated policy checks (fmt / validate / plan security review) and drift detection. Delivered >70% faster provisioning (days→hours) while reducing infrastructure cost footprint through rightsizing & reusable patterns and enhancing audit traceability for compliance.

Developed a full-stack reporting application deployed behind a GCP global HTTP load balancer aggregating multi-region OMS datasets (orders, invoices, inventory, credit memos, product audit trails). Implemented role-based access controls and metric-driven dashboards enabling unified visibility and reducing manual reconciliation effort.

Implemented an attention-augmented CNN architecture for hyperspectral super-resolution achieving 2× spatial enhancement , ~4 dB PSNR gain and >90% spectral similarity. Ranked 2nd among 20+ capstone projects demonstrating optimized trade-offs between spectral fidelity and reconstruction latency.

Executed 1,000+ Monte Carlo simulations applying multi-factor discount rate sensitivity and stochastic price curves to derive 10-year NPV distributions for a manufacturing investment case. Built an interactive Power BI executive dashboard surfacing risk bands, sensitivity sliders, and scenario comparatives to accelerate decision cycles.

Modernized a legacy core banking stack by introducing a microservices architecture using Quarkus (Java) for low-memory JVM services and ASP.NET Core for high-throughput APIs. Implemented event-driven messaging and containerized CI/CD pipelines, reducing deployment time by ~30% while improving horizontal scalability and operational observability.