Developed and optimized autonomous forecasting agents capable of analysing structured JSON data for strategic predictions.

Designed and deployed code-executing agents to automate insight generation, directly aiding executive decision-making.

Led root cause analysis (RCA) initiatives to uncover performance variance drivers, improving long-term process efficiency.

Cut LLM inference costs by 82% through architectural redesign and streamlined output workflows.

Built modular AI agent systems using LangChain, Phidata, Agno, and CrewAI for scalable, maintainable automation.

Engineered backend automation for a parameterized chatbot, enabling GUI-independent forecasting capabilities.

Reduced agent runtime from 15 minutes to 41 seconds through task parallelization and efficient architecture.

Secured data pipelines using cryptographic standards like Fernet to ensure end-to-end data integrity.

Improved accuracy and relevance of insights by refining JSON-driven data extraction and transformation workflows.

Played a key role in integration planning, delivering insights that powered business automation and strategic pivots.

Integrated LangGraph into modular AI agent pipelines to automate workflow orchestration for over 8 projects, reducing latency by 60% and enabling scalable, event-driven data processing over a 6-month deployment.