I'm Vijay — a solution architect and principal engineer building AI systems and the software around them. I work across architecture, product engineering, and production infrastructure, with a focus on systems that need to work beyond the demo.
A feature is the part that demos. The system is everything that has to hold once it meets real data and real load — the retrieval boundary, the fallback path, the audit trail, the deployment you can roll back. That is the part I am actually hired for, and the part that decides whether the feature still works in six months.
RESTful and GraphQL APIs, concurrent systems, data integrity under load. Where the discipline of correctness was built.
RESTGraphQLConcurrencyPostgreSQL
SAP, ERP and legacy system integrations. Data synchronization across boundaries. Working with constraints that billions of dollars of business logic depend on.
SAPERPData pipelines
End-to-end systems from React front ends to Python and Java back ends. Designed for maintainability and real production loads, not demos.
ReactNext.jsTypeScriptPythonJava
RAG pipelines, LLM integrations and multi-agent workflows for enterprise clients. Controlled prompts, audit trails and explainable reasoning — designed for teams that need to trust their AI.
RAGAgentsLLM orchestrationPrompt & context engineering
A product and AI engineering studio. I lead the engineering, and a small senior team builds alongside me — no layers between the person who designs a system and the people who build it.
ArchitectureProduct engineeringProduction systems
BuiltRetrieval-augmented generation with controlled knowledge retrieval and answer validation.
WhyTeams needed reliable answers from internal documents without hallucinations.
Faster knowledge access, deployed with monitoring
TechnologyNext.jsPythonVector DBLLM APIs
BuiltAn agent-based system with explicit decision boundaries, tool access and fallback paths.
WhyMulti-step processes had required manual coordination and ran inconsistently.
Less manual coordination, with full audit trails
TechnologyPythonAI agentsTypeScriptPostgreSQL
BuiltOptimized data pipelines, a caching strategy and a clean API boundary between services.
WhyHigh traffic handling and complex business logic, without losing performance.
Performance held at scale
TechnologyReactNext.jsTypeScriptPythonJava
BuiltMulti-agent orchestration that evaluates resumes, requirements and role-specific context.
WhyOptimizing a resume across different roles was slow and inconsistent.
Repeatable, explainable evaluation
Problem framingConstraintsArchitecture decisions
ReactNext.jsTypeScriptAccessibilityPerformance
PythonJavaNode.jsFastAPIRESTGraphQL
RAGVector databasesLLM orchestrationAgent workflowsPrompt & context engineering
PostgreSQLRedisData pipelinesSAP / ERP integration
Scalable architectureObservabilitySecurity-aware design
Systems must earn trust before they earn features.
Reliability is architecture — not an afterthought.
Code that cannot be read cannot be maintained.
Every abstraction must justify its existence.
Deployment, security and observability are designed with the system, not bolted on at the end.
Approach shows where each of these lands in the process.
Somebody depends on the thing being right. A retrieval system that invents an answer, a workflow that quietly drops a step, a platform that fails at the wrong moment — none of those are technical inconveniences. They are somebody's decision, or afternoon, or job. Controlled prompts, audit trails, fallback paths and explainable reasoning are how you take that seriously.
The architecture behind each of the four systems above — written up in full on the Work page.
If you're working on a system where architecture, product, and AI need to come together, let's talk.