Jay Ghiya
Builder. Engineer
Currently building unoplat-code-confluence — an open source code intelligence platform for better AI workflows.
Hi, I'm
About Me
I’m a Full Stack Engineer, AI Consultant, and the Founder & CEO/CTO of Unoplat Technologies — with 8+ years of experience shipping production systems at scale.
I spent my early career deep in backend and distributed systems at GE Healthcare, building microservices, real-time streaming pipelines, and data platform architecture across Kafka, Kubernetes, and cloud-native stacks. From there, I moved to Intuit as a Senior Software Engineer, sharpening my craft in product engineering.
Along the way, I expanded into full-stack development and deployment, product design, and Agent Development.
Today, I’m building unoplat-code-confluence — an open-source code intelligence platform that uses formal grammars, structured schemas, and AI to make codebases truly readable for both humans and agents. Think of it as the bridge between raw code and reliable AI workflows — built on precision, not guesswork.
As a Full Stack AI Consultant, I help teams integrate AI into real engineering and business workflows, while also owning end-to-end product design across frontend and backend. Whether it is designing RAG pipelines, architecting autonomous agent systems, building AI-native design systems, or shipping scalable cloud-native backends, I work across the stack to accelerate product timelines and optimize systems, processes, resources, and infrastructure—with a strong focus on reliability, auditability, and scalability.
Technologies & Skills:- React & TypeScript
- Python
- Design Systems & UX
- Backend Engineering
- Docker & Kubernetes
- CI/CD Pipelines
- Observability
- Retrieval-Augmented Generation
- Fine-tuning Techniques
- Autonomous Agents
- Vector Databases
- Product Engineering
- Project Management
Experience
Founder - CEO/CTO
-
Unoplat
Sep 2024 - Present
Building unoplat-code-confluence — an open-source code intelligence platform that uses formal grammars, structured schemas, and AI to make codebases truly readable for both humans and agents.
Senior Software Engineer
-
Intuit
Sep 2022 - Sep 2024
Worked on the People Platform team, building and scaling real-time ingestion pipelines for core people data.
- Designed and built real-time ingestion pipelines for the People Platform
- Drove architectural improvements and better design patterns across existing services
- Championed OpenTelemetry adoption to improve observability across the platform
- Established performance testing frameworks and practices for pipeline reliability
Senior Software Engineer
-
GE Healthcare
Apr 2022 - Sep 2022
Cofounder - Data Platform. Leading Data Fabric vertical responsible for developing platform microservices, real-time streaming and batch applications.
- Led Data Fabric vertical with microservices, message brokers (Kafka), and real-time applications
- Contributed to Data Validation vertical integrating validation stages as part of GitOps
- Researched message broker algorithms and operators for Data Infra vertical
- Led Data Mesh Integration to 3rd-party components using GitOps
Software Engineer
Aug 2019 - Apr 2022
Involved in design, development, testing, and integration of microservices, real-time streaming, and batch applications for Clinical Care Solutions.
Edison Engineer (EEDP)
Jul 2017 - Jul 2019
Edison Engineering Development Program — GE’s premier early-career leadership program focused on engineering excellence and innovation.
Intern
Jul 2016 - Jul 2017
Life Care Solutions / Clinical Care Solutions division.
Featured Projects
Python
Workflow
TypeScript
Code Context
Code Parsing
Dependency Track
Code Understanding
LLM
Gen AI
Hallucination Mitigation
Agentic Planning
Agentic AI
Agent Skills
Agents MD
Precision
Reliability
Unoplat Code Confluence
A codebase intelligence platform that uses formal grammars and schemas to create reliable, structured context from your code. Unlike brittle heuristics or LLM guesswork, it enables reliable, auditable, and efficient AI workflows built on trustworthy foundational context.
Core Values:
- Precision: Grammar-based analysis, not regex or LLM guesswork
- Efficiency: Process large codebases without drowning in noise
- Reliability: Consistent results you can trust
- Self-hosting: Your code stays on your infrastructure
- Transparency: Open Source
First AI Use Case: Generating a comprehensive AGENTS.md per repository that captures sections like Development Workflow, Dependencies Guide, and Business Logic through deterministic code grammar and agents, with incremental auto-updating on the roadmap.
Second AI Use Case: Automatic skill recommendation and installation — intelligently suggests and installs relevant agent skills based on your codebase context, so AI workflows are always equipped with the right tools without manual configuration.