Led development of an AI-driven assistant embedded into admin workflows, reducing merchant task completion time by 35% through automated multi-step execution flows.

Launched intelligent workflow automation capabilities that improved merchant productivity by 30% using natural language-driven actions across analytics, marketing, and store management systems.

Increased task success rate by 25% by implementing structured AI decision flows, guardrails, and human-in-the-loop confirmations, improving trust and reducing execution errors.

Defined product vision, roadmap, and prioritization using RICE and data-driven frameworks, aligning AI/ML, engineering, and design teams to deliver scalable platform features.

Partnered with AI/ML teams to optimize LLM-based systems through prompt engineering, evaluation pipelines, and feedback loops, improving accuracy, latency, and usability.

Designed end-to-end AI-powered user journeys that balanced automation with user control, improving UX clarity and feature adoption through conversational interfaces and guided workflows.

Built experimentation frameworks using A/B testing and analytics to measure performance, optimize engagement, and iterate on AI-driven features using user behavior insights.

Managed cross-functional stakeholders across engineering, sales, and leadership, resolving ambiguity and driving execution in fast-paced environments.

Leveraged GCP services including Vertex AI, BigQuery, and Cloud Functions to enable scalable AI workflows, real-time data processing, and model monitoring.

Applied cloud-native architecture principles using GCP, Kubernetes, and microservices to ensure scalable, low-latency, and highly available AI-powered systems.

Improved first-attempt delivery success rates by 18% and reduced cost per shipment by 12% by leading route optimization initiatives across Tier-2/3 markets using data-driven product decisions.

Scaled last-mile delivery capabilities across 100+ cities, improving SLA adherence by 20% through optimized clustering strategies, partner network expansion, and real-time routing enhancements.

Increased delivery efficiency and reduced failed deliveries by 15% by introducing driver feedback loops, improving route adherence, and aligning product strategy with on-ground operational constraints.

Defined and executed product roadmaps using data analytics, A/B testing, and KPI-driven prioritization, balancing trade-offs between cost, speed, and user experience in logistics systems.

Collaborated cross-functionally with engineering, data science, and operations teams to deliver scalable routing solutions, leveraging machine learning insights, geospatial analytics, and real-time decision systems.

Designed mobile-first experiences for delivery partners with focus on vernacular UX, usability, and accessibility, improving adoption and reducing friction in low digital literacy environments.

Applied experimentation frameworks, hypothesis-driven development, and product analytics tools to validate features, optimize performance, and continuously improve delivery outcomes and operational efficiency.

Built and scaled distributed, event-driven systems on AWS leveraging services like Lambda, S3, and Kinesis, enabling real-time routing updates and high-throughput delivery tracking pipelines.

Partnered with engineering to deploy scalable microservices and ML-powered routing models on AWS infrastructure using ECS, API Gateway, and DynamoDB for resilient, low-latency logistics operations.

Utilized AWS-based data pipelines and analytics tools including Redshift and Glue to process delivery data, generate insights, and support data-driven product decisions at scale.