Designing AI systems.Shipping them to production.
From intent to production — with AI as a co-developer and human judgment in control.
I optimize for time-to-value: production-ready systems, not prototypes.
MY PHILOSOPHY
Code is no longer the constraint. Thinking is.
I believe the most powerful developers of this era won't be defined by languages they know, but by systems they can imagine. From my first React component to integrating Google Gemini into a voice assistant, I've learned that the real skill is knowing what to build — AI handles the how.
I use AI as a co-developer. Every project in my portfolio was built with AI-assisted development — not as a crutch, but as a force multiplier. This isn't about replacing skill with prompts. It's about amplifying human judgment with machine execution.
Prompt engineering is architecture.
Iteration is intelligence.
Shipping is the only metric.
How I Build
A systematic approach to AI-assisted development
Idea & Intent
Define the outcome, not the output. Clarify user needs and success criteria.
AI-Assisted Research
Use LLMs to explore architectures and generate initial specs
Prompt Engineering
Translate vision into structured prompts for AI generation
System Architecture
Design data flows and APIs. Human decisions, AI documentation.
AI-Assisted Generation
Frontend, backend, and database code. Human-reviewed, human-refined.
Test, Iterate, Ship
Debug with AI, iterate on feedback, deploy to production.
Projects
AI-powered solutions, from concept to production
About
I'm Sagar Wavhal — an AI Engineer focused on GenAI and AI application development. I design and ship AI applications end to end, using AI as a co-developer and human judgment as the control layer.
- B.E. Computer Engineering — July 2026, CGPA 7.53
- Built and shipped 7 production systems
- Focused on time-to-value, not prototypes
- Comfortable owning systems end-to-end
- Believe AI amplifies thinking, not replaces it
I care less about tools and more about outcomes.
Resume, distilled.
Education, experience, focus, and proof — at a glance.
7
Projects shipped
7.53
CGPA
Jul 2026
Graduated
Best Paper
INMEC 2026
Education
Bachelor of Engineering in Computer Engineering (CGPA: 7.53)
P.D.E.A.'s College of Engineering · July 2026
Experience
Project-Based Web Developer
S.K. Industries · Mar 2026 – Aug 2026
Live platform: React admin app + Express API (invoices, GST, AI extraction)
skindustries.vercel.appCurrent Focus
- GenAI development
- AI-assisted full-stack applications
- System-oriented project building
- Learning ML & LLM foundations
Highlights
- Best Paper Award — INMEC 2026Co-authored “AIVO: AI-Powered Search Optimization”
- Innovate You Techathon 2.0 WinnerFirst place — AI + Blockchain solution
- freeCodeCamp CertifiedResponsive Web Design
AI & GenAI Skills Stack
The tools and techniques I use to build intelligent systems
AI / GenAI
- LLMs
- Generative AI
- RAG
- Gemini
- OpenAI
- Groq
- Ollama
- Prompt Engineering
- LLM Evaluation
Agentic AI
- Agentic Workflows
- AI-Assisted Automation
- Tool/API Integration
- Automated Remediation
- Prompt Injection Defense
Machine Learning / Data Science
- Scikit-learn
- Isolation Forest
- Anomaly Detection
- NumPy
- Pandas
- SQL
Languages
- Python
- TypeScript
- JavaScript
- HTML5
- CSS3
Backend / AI APIs
- FastAPI
- Node.js
- Express
- REST
- WebSocket
- Pydantic
- JWT
- OAuth 2.0
Frontend
- React
- Next.js
- Vite
- Tailwind CSS
- Redux Toolkit
- Three.js
- GSAP
- PWA
Data / Cloud / DevOps
- MongoDB
- Redis
- Docker
- Git
- GitHub Actions
- CI/CD
- Vercel
- Render
- Sentry
- Playwright
Learning & Credentials
Engineering fundamentals meet AI specialization — building the foundation to design, ship, and scale GenAI systems.
Bachelor of Engineering in Computer Engineering
P.D.E.A.'s College of Engineering, Pune
Completed July 2026 · CGPA 7.53
- →Integrating LLM APIs into production applications
- →Building AI-assisted development workflows
- →Prompt engineering for code generation
- →Architecting offline-first AI systems
Diploma in Data Science, Machine Learning & Generative AI
Gamaka AI
In Progress
Building foundational ML & Data Science knowledge to complement AI-native development — the core layer behind every GenAI system.
- →Data Science & Machine Learning fundamentals
- →Generative AI architecture & applications
- →LLM internals & prompt engineering depth
“Most GenAI courses teach API wrappers. This builds the core — statistics, ML pipelines, and model internals — so I can architect systems, not just consume them.”
WHAT I'M BUILDING TOWARDS
→AI agents that reason, not just respond
→Systems that learn from their outputs
→Interfaces that anticipate intent
→Tools that make human creativity limitless
Development is being rewritten.
I'm here to help write the next chapter.
Let's build something intelligent.
Open to collaborations, internships, and ambitious projects.
© 2026 Sagar Wavhal | AI Engineer