Building and operating production-grade ML, LLM, and agentic systems across AWS, Azure, Databricks, and enterprise data platforms.
I’m an AI/ML engineer with 13+ years of experience designing, building, and operationalizing machine learning and generative AI solutions across cloud and on-prem environments. My work spans MLOps, LLMOps, cloud-native deployment, agentic workflows, and enterprise data platforms.
Over the last several years, I’ve helped product and analytics teams turn AI ideas into production systems with clear architecture, monitoring, automation, and operational governance. My experience covers healthcare, banking, finance, retail, and product optimization use cases, with a strong bias toward scalable engineering and measurable business value.
End-to-end development of predictive models, pricing optimization, churn analysis, risk models, recommendation systems, and feature-driven analytics pipelines.
LLM-powered applications, retrieval workflows, summarization, and production AI experiences designed for business workflows and knowledge retrieval.
Operationalization of models through automation, experiment tracking, monitoring, model governance, CI/CD, and cloud workflow orchestration.
Designing AI systems on enterprise data platforms with strong focus on integration, storage, processing, and platform-level reliability.
Multi-agent automation using CrewAI and n8n for research, summarization, e-commerce price intelligence, travel planning, and workflow-driven content generation.
Repository focused on LLM evaluation, vector databases, deployment patterns, and operational practices for production-grade language model systems.
Reference architecture and implementation guidance for building end-to-end ML pipelines with deployment, monitoring, experiment tracking, and production operations.
Automated ML delivery pipeline using GitHub Actions and Databricks to enforce test gates, sync code, and manage deployments between development and production environments.
Exploration of deep learning workflows and model development in PyTorch, covering common architectures and hands-on experimentation.
Modeling the impact of marketing levers to support budget allocation and business planning using regression-based media mix approaches.
Price prediction system for listing attributes, city-level features, and occupancy factors using ensemble learning and regression models.
Employee attrition analysis designed to identify the business drivers behind retention risk and support more informed workforce decisions.
