Mohan Balasubramaniyam | Software Engineer

Profile

Enterprise engineering with an AI-forward direction.

My experience is rooted in solving real business problems: implementing UI behavior, integrating APIs, connecting commerce platforms, debugging cross-system issues and supporting production workflows. I am now applying the same practical approach to Generative AI.

Engineering strengths

Full-stack implementation, API integration, business-rule development, commerce workflows, troubleshooting and translating evolving requirements into maintainable software.

AngularTypeScriptNode.jsREST APIsShopifyGraphQL

Current AI focus

Building practical capability around private/local LLMs, document-grounded RAG, output evaluation and agentic workflow automation rather than AI as a standalone demo.

LLMsRAGOllamaAI EvalsPrompt EngineeringAgents

Technical expertise

A recruiter-friendly view of my stack.

Grouped by competency so the technologies are easy to scan and reflect how I use them in real application work.

Frontend

Angular · TypeScript · JavaScript · HTML5 · CSS · Responsive UI · Component Development

Backend & APIs

Node.js · Express · REST APIs · JSON · Validation · Error Handling · API Integration

Commerce & Integration

Shopify · Admin GraphQL · Webhooks · Metafields · Order Synchronization · WCS Integration

API & Developer Tools

Postman · Swagger / OpenAPI · Git · Debugging · Payload Analysis · Integration Testing

Generative AI

LLMs · Retrieval-Augmented Generation (RAG) · Ollama · Prompt Engineering · AI Evals · Local AI

Automation

AI Agents · Workflow Automation · Scheduled API Validation · System-to-System Process Automation

Experience

Professional foundation.

A concise view of the type of engineering work I handle. Exact role titles and dates can be expanded as the public résumé is finalized.

Current experience
Chennai, India

Royal Cyber Inc.

Enterprise Software Engineering

Working across frontend applications, APIs, product configuration, commerce integrations and production troubleshooting.

Implement and maintain Angular/TypeScript application features driven by complex business rules.
Integrate frontend and commerce workflows with backend REST services and enterprise systems.
Work with Shopify Admin GraphQL, webhooks, metafields and order synchronization patterns.
Validate cross-system behavior including payload consistency, lead-time logic, pricing and order processing.
Troubleshoot defects across UI, API and downstream integration layers.

Current growth area

Generative AI & Intelligent Applications

Hands-on learning and product experimentation

Developing practical knowledge of local LLMs, RAG, AI-assisted evaluation and automation with a focus on integrating AI into business software.

Selected work

Engineering problems, not generic demos.

These examples show the type of systems thinking, integration work and application design I bring to projects.

Enterprise Commerce

Shopify → WCS Order Integration

Integration workflow for transferring Shopify orders into an enterprise commerce backend and maintaining synchronization visibility.

ProblemMove orders reliably between platforms while preserving downstream identifiers and supporting future tracking updates.

My contributionOrder payload mapping, Admin GraphQL queries, metafield persistence, synchronization logic and integration troubleshooting.

Node.jsShopifyGraphQLWebhooksMetafieldsWCS

Product Configuration

Builder Lead-Time & Pricing Integration

Business-critical lead-time behavior across configurable products and checkout flows.

ProblemKeep lead-time calculations consistent across style overrides, rush rules, builder behavior and checkout results.

My contributionAPI integration, payload validation, business-rule debugging, regression testing and cross-flow issue analysis.

AngularREST APIsBusiness RulesDebuggingQA

AI Application

LLM Output Evaluation Platform

A structured evaluation workflow for comparing generated presentation outputs against prompts and supporting material.

ProblemEvaluate multiple PPT outputs consistently instead of relying on vague, repetitive AI comments.

Solution directionQuestion-based 0–7 scoring, detailed justification, document context and slide-by-slide analysis.

GenAIAI EvalsPrompt AnalysisFile AnalysisScoring

Full-Stack Product

Appointment & Consultation Platform

Booking experience covering user details, slot selection, payments and meeting creation.

ProblemCreate an end-to-end consultation workflow while avoiding duplicate meeting links and handling production integrations reliably.

Solution directionBooking UI, payment flow, Google Calendar/Meet integration, deployment and production bug-fix workflow.

Web AppCalendarGoogle MeetPaymentsHosting

Applied AI

AI connected to useful systems.

My AI direction builds on existing software-engineering experience: secure data flows, integration reliability, measurable output quality and practical business use cases.

Currently exploring

RAG ArchitectureVector DatabasesAI Agents LLM EvaluationLocal LLMsMCP

Private Document AI AssistantLocal LLM + RAG workflow for answering questions from private documents without depending entirely on model memory.

AI Presentation EvaluationStructured scoring and slide-level review of AI-generated presentation outputs against prompts and source material.

Local AI with OllamaHands-on experimentation with running smaller language models locally for privacy, learning and offline use cases.

Agentic Workflow AutomationExploring how AI agents can coordinate APIs, documents and repetitive operational processes.

Education

Academic background.

Formal computer applications education supported by hands-on enterprise software experience and continuous technical learning.

2007 — 2010

Master of Computer Applications (MCA)

Government Arts College, Coimbatore.

Continuous learning

Enterprise Engineering + Generative AI

Ongoing practical learning across integrations, modern commerce APIs, LLMs, RAG, evaluation and automation.