Rohan

I specialize in designing large-scale automation systems, orchestration layers, and resilient test engineering frameworks. With deep experience in backend automation, distributed test architecture, cloud-native validation platforms, and end-to-end quality systems, I focus on building frameworks that scale across teams, environments, and marketplaces.

I am also strongly focused on AI-driven automation. I actively work on leveraging LLMs, autonomous agents, and AI-assisted quality engineering to accelerate system validation, reduce engineering effort, and introduce self-healing test ecosystems.

Designed an orchestration engine that consumes orders from SQS/Kafka, performs fan-out API calls with retries, fallbacks, circuit breakers, and consolidated response mapping. Built for high throughput and horizontal scalability.

Created a modular shadow framework supporting ingestion normalization, distributed execution, and transaction-level comparisons across System1/System2. Introduced data filtering and configuration decoupling to reduce setup time.

Developed an LLM-powered classification engine that analyzes failures, clusters patterns, and suggests root causes. Integrated with CI pipelines for continuous learning and triage automation.

Architected a test harness capable of running parallel contract tests, chaos scenarios, data-driven validation, and real-time metrics aggregation.

Built a Kubernetes‑based Selenium/Grid cluster with auto-scaling nodes, service mesh routing, and on-demand test environments.

Designed a system that maps code changes to test suites using dependency graphs + ML predictions, enabling selective execution and reducing test cycles by 80–90%.

I actively integrate AI into engineering workflows: autonomous test agents, natural-language-driven frameworks, dynamic data generation, and predictive stability scoring. My goal is to build next-generation automation ecosystems where tests adapt, heal, and evolve using AI.