Extended Python microservices within the agent orchestration layer to implement task routing, state validation, and execution safeguards for multi-agent systems, automating 12+ end-to-end supply chain workflows and reducing multiday manual reporting cycles to ~15-minute automated runs.

Engineered RAG pipelines integrating OpenAI and Anthropic APIs for structured data extraction from multi-page enterprise PDFs, improving extraction accuracy from ~60% (rule-based) to 90%+ and eliminating a manual QA step across 3 supply chain workflows.

Tuned vector-based retrieval pipelines across a corpus of 1M+ enterprise documents, sustaining sub-200ms semantic search latency under production load.

Authored and maintained 200+ TypeScript C3 Types and Methods to federate SAP and Salesforce data into a unified enterprise state model, supporting concurrent processing of 50,000+ daily business entities.

Built modular React components for C3 AI Studio-including visual workflow editors-reducing manual configuration overhead and improving deployment velocity for Solutions Architecture teams.

Refactored Java/Scala workflow execution services-replacing N+1 query patterns with batch fetching and introducing read-through caching - reducing average data access latency by 2x under peak concurrency and eliminating 3 recurring timeout-class incidents.

Standardized containerized deployments to AWS EKS using Helm and GitLab CI, reducing deployment errors and enforcing consistent configuration across APA environments.

Integrated workflows with AWS S3, SQS, and Kinesis to support ingestion of 10,000+ events/sec, enabling asynchronous AI agent triggering for near-real-time anomaly detection.

Led root-cause investigations on 10+ production incidents using CloudWatch metrics and ELK log analysis, identifying and resolving systemic bottlenecks that reduced mean time to resolution (MTTR) by ~25% across APA environments.

Owned and evolved backend services in Java (Spring Boot) and Python (FastAPI) for UPI payment workflows, managing transaction initiation, retries, failure handling, and final state reconciliation in high-volume production systems.

Architected and owned transaction state machine logic (INITIATED → PROCESSING →

SUCCESS/FAILED/REVERSED) for 100K+ daily transactions, enforcing exactly-once semantics using Kafka-based retry queues and Redis-backed idempotency keys.

Identified and resolved critical payment edge cases-duplicate callbacks, network timeouts, and partial failure recovery -working with product, risk, and compliance teams to reduce production escalations.

Designed and optimized MySQL schemas and high-volume transactional queries (transaction, audit, settlement tables) on Azure-managed databases, leading schema migrations and performance tuning initiatives that improved reconciliation throughput during peak UPI load.

Investigated and resolved P0/P1 production incidents using structured logging, metrics, and distributed tracing; performed root-cause analysis and implemented long-term fixes to strengthen system reliability.

Owned CI/CD pipeline configuration in Azure DevOps for Docker-based services, standardizing promotion workflows across dev/staging/production and enabling zero-downtime rollouts with automated rollback triggers.

Built idempotent REST APIs with JWT-based service authentication and RBAC, preventing double debits and securing internal payment and audit APIs against unauthorized access.