IT Professional with 9+ years of experience in Data Engineering, Software Development, and Machine Learning. Expert in building scalable data pipelines, ML systems, and Agentic AI solutions across sustainability, finance, retail, and ad-tech domains.
Specialized in building scalable data infrastructure, ML systems, and cutting-edge AI solutions
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Developed Agentic AI-powered sustainability assistant using LangChain with RAG. Built scalable ingestion pipelines from 40+ sources. Led Snowflake to Databricks migration using medallion architecture.
Architected and deployed Apache Airflow across 17 environments for Apple's Ad Platforms. Built ETL test automation frameworks reducing manual testing by 60%.
Engineered highly scalable ETL/ELT pipelines with Airflow and Snowflake. Integrated ML recommendation outputs with Microsoft Dynamics 365 using RESTful APIs.
Orchestrated data pipelines into OpenML-based fraud risk engine. Integrated DVC with S3 for dataset versioning. Built complex DAGs in Apache Airflow.
Developed distributed big data pipeline for predicting energy consumption using Apache Spark, XGBoost, and LSTM. Achieved 15% improvement over baseline models.
Reduced asset maintenance costs by 30% with real-time anomaly detection using CART and ARIMA. Built streaming pipelines with Kafka and InfluxDB.
Delivered Exclusive Growth Hacks & Enterprise Solutions
Built AI-powered assistant using LangChain with RAG, retrieving carbon emissions metrics from Databricks vector store for regulatory-compliant sustainability reporting.
Aggregated SonarQube, GitHub, Jenkins metrics into Delta Lake to compute engineering KPIs, enabling automated delivery of team health dashboards to leadership.
Architected Apache Airflow across 17 environments (on-prem and AWS) with Terraform IaC, supporting scalable DAG executions for Apple's Ad Platforms.
Orchestrated pipelines from Snowflake, Data Lake, and Elasticsearch into OpenML-based fraud risk engine, enabling real-time fraud score computation.
Developed ML pipeline using XGBoost, LSTM, and Prophet for time-series forecasting of campus energy consumption, achieving 15% improvement over baseline.
Led Snowflake to Databricks migration implementing raw, bronze, silver, gold layers with Z-ordering and liquid clustering for optimized query performance.
Deep dive into the technical architecture of enterprise-scale Data Engineering and ML systems
Nike Sustainability Foundation
LangChain-powered RAG system retrieving carbon emissions metrics from Databricks vector store for regulatory-compliant sustainability reporting. Features multi-agent orchestration and real-time data retrieval.
Fidelity Investments
Multi-source data ingestion platform aggregating metrics from SonarQube, GitHub, Jenkins, and Jira into Delta Lake. Implements medallion architecture with automated KPI computation and leadership dashboards.
Apple Ad-Platforms
Enterprise-scale Apache Airflow deployment across 17 environments (on-prem and AWS). Terraform-based IaC enabling scalable DAG executions with Kubernetes orchestration.
Capital One
Streaming data pipeline orchestrating data from Snowflake, Data Lake, and Elasticsearch into OpenML-based fraud risk engine. Enables real-time fraud score computation with sub-second latency.
Nike Sustainability Foundation
Snowflake to Databricks migration implementing medallion architecture with Z-ordering and liquid clustering. Features raw, bronze, silver, and gold layers with automated data quality checks.
Deep dives into Data Engineering, ML, AI, and emerging technologies
Learn the core concepts of data engineering, from ETL pipelines to data warehousing, explained in simple terms.
Demystifying blockchain technology and cryptocurrencies with simple explanations and real-world applications.
A comprehensive guide to building, training, and deploying machine learning models in production environments.
Understanding neural network architectures and when to use CNNs, RNNs, LSTMs, and Transformer models.
Exploring how Large Language Models work, from transformers to prompt engineering and fine-tuning.
Learn how RAG combines retrieval systems with LLMs to create accurate, domain-specific AI applications.