• Develop AI/ML solutions using Python, LLMs, RAG, NLP, and predictive analytics to automate business processes. • Build Generative AI and Agentic AI applications including intelligent assistants, knowledge retrieval, and workflow automation. • Design scalable data pipelines and ML models using SQL, Spark, cloud platforms, and modern data engineering tools. • Implement MLOps/LLMOps pipelines for model deployment, monitoring, evaluation, governance, and continuous improvement. • Collaborate with technical and business teams to deliver AI-driven solutions that improve efficiency, automation, and customer experience.

• Built a hybrid recommendation engine using collaborative filtering, content-based filtering, and semantic embeddings, increasing CTR by 18% and driving 25% of orders through recommendations. • Developed a production-grade Agentic AI assistant using RAG, ReAct, Guardrails, and LLM Gateway to automate customer order, shipment, delivery, and account inquiries. • Built competitive pricing and demand forecasting models, contributing to $3.2M+ annual incremental sales, 15% higher customer retention, and $1.1M annual inventory cost savings. • Led Azure migration, enterprise data modeling, and cloud cost optimization, achieving 30% ETL performance improvement and $250K annual savings. • Established enterprise MLOps/LLMOps pipelines for deployment, evaluation, monitoring, and governance, reducing model release cycles by 60%.

• Developed predictive models using Spark, PCA, and time-series analysis, improving forecasting accuracy from 23% to 94% and reducing operational deviations. • Built an AI-powered SQL chatbot that enabled users to generate complex SQL queries through natural language, reducing query resolution time by 50%+. • Developed ETL pipelines using Azure Data Factory and ADLS Gen2, reducing pipeline maintenance costs by approximately 20%. • Built predictive analytics solutions using Vertex AI, Snowflake, and Azure Synapse, improving model and dashboard performance by 30%. • Created Power BI dashboards with DAX and Power Query, improving decision-making by 25% and contributing approximately $2M/year in efficiency gains.

• Developed radar–LiDAR sensor fusion models that improved flight-delay prediction accuracy by 10%, contributing to approximately $3M/year in reduced delay costs. • Fine-tuned CNN models for real-time landing-gear wear detection, achieving an R² of 0.85 and strengthening aircraft safety inspections. • Built PySpark ETL pipelines on Databricks to process large-scale flight telemetry data, reducing manual data handling by 40%. • Integrated real-time weather, altitude, radar, and sensor data for predictive analytics, reducing data processing time by 20%. • Collaborated with aviation engineering, safety, and operations teams to improve predictive analytics reliability and reduce diagnostic errors by 25%.

• Analyzed e-commerce customer and sales data, improving Revenue Per Visitor by 9% through targeted advertising and A/B testing strategies. • Developed scalable ETL pipelines using Azure Data Factory and PySpark, improving data processing speed by 30%. • Built data warehouses using Azure Synapse Analytics and Hadoop to support large-scale analytics and business intelligence reporting. • Created Tableau dashboards for sales and compensation analytics, improving reporting efficiency by 20% and project completion rates by 15%. • Conducted sentiment analysis and advanced statistical analysis to identify business trends and support strategic decision-making.