Yadnyesh Mulay: I build useful things
and ship them with ai.
Full-stack product builds, AI integration, business/AI transformation consulting, and hands-on teaching for students and professionals.
Your idea. your users.
Your product.
I take ideas from a first sketch to a working product, building the full stack, weaving in AI where it earns its place, and helping people grow along the way.
Build
Full-stack apps, MVPs & prototypes
Enhance
LLM integration, RAG & automation
Consult
AI transformation roadmaps & process optimization
Teach
Mentoring students & upskilling professionals
Where I've worked.
MahaWiki: Tech Community
Running free, industry-oriented training, webinars, and workshops to make technical education accessible.
Cloud Community Group
Organising cloud events, bootcamps, and mentorship for developers and students.
SkillShip Foundation
Led expert sessions and skill-development workshops across colleges and companies.
The real index
Every entry names its own role and tech honestly, no invented metrics, no borrowed logos. Repositories verified under github.com/MYadnyesh.
- 04Twitter Sentiment Detector (Capstone)Source
T-Shaped Engineering
Vertical depth in AI/LLMs. Horizontal breadth across Cloud, Full Stack, Product/UX, and Business Consulting, resting on software engineering fundamentals.
AI / LLMs
Deep specialization in LLM application development: prompt engineering, RAG systems, agentic workflows, multi-modal AI, and AI-native product architecture.
The full toolkit
A wall of what I build with, grouped by where it sits in the stack. Hover any row to slow it down.
AI Systems Engineering
AI isn't a bolt-on feature, it's an integrated engineering layer, from API call to production workflow.
LLM APIs
Integration & orchestration
- LLM APIs (OpenAI, Gemini, Claude)
- Prompt Engineering
Retrieval
Grounding & context
- RAG & Embeddings
- AI Automation
Agents & Workflows
Autonomous reasoning & orchestration
- AI Agents
- Multimodal AI
- Voice AI
- Agentic Workflows
Infrastructure
Local inference & serving
- Ollama / Local LLMs
- MCP (Model Context Protocol)
- OpenCode / Codex / OpenClaw
- LangGraph / LangChain
- CrewAI / Multi-Agent
- Coding Agents
- Model Routing
- Inference / Quantization
- Fine-tuning / LoRA
- AI Infrastructure
- Voice Agents
- n8n / Workflow Automation
AI is a capability, not the product
LLMs enable better products, they don't replace product thinking. The value is in what the system enables users to do.
Evals before vibes
Systematic evaluation beats prompt tweaking. Build eval infrastructure first.
Ground in reality
RAG, citations, and verification aren't optional. Hallucination is a feature of the architecture, not a bug to prompt away.
Design for the failure modes
Latency, cost, rate limits, degradation. Graceful degradation beats a perfect happy path.
Human + AI beats AI alone
Best systems keep humans in the loop for judgment, creativity, and accountability.
Start simple, scale deliberately
Single LLM call → RAG → Agent → Multi-agent. Add complexity only when the simpler layer is proven insufficient.
Inpublic
The visible layer: shipped projects, open-source repositories, community work, and teaching. Everything you can check before we ever talk.
In thelab
The experimental layer: local LLMs, agentic workflows, MCP, multi-agent systems, and inference. Exploration that feeds the next thing I build.
In the lab
Technical directions I'm investing time to understand deeply, ahead of production use. Deliberate exploration, not unfinished work.
Local LLMs & Inference
Running models locally with Ollama, experimenting with quantization (GGUF), model routing, and offline-first AI architectures.
Agentic Coding Workflows
Evaluating OpenCode, Codex, OpenClaw, and MCP for autonomous code generation, refactoring, and multi-file edits.
Multi-Agent Systems
Building collaborative agent networks with LangGraph and CrewAI, planning, delegation, and verification loops.
AI Infrastructure & Serving
Model serving (vLLM, TGI), batch inference optimization, KV caching, and cost-effective deployment patterns.
Voice AI & Real-time Agents
Speech-to-text, LLM reasoning, text-to-speech pipelines with latency optimization for conversational agents.
Workflow Automation (n8n)
Visual workflow builder for AI-augmented business processes, connecting APIs, databases, and LLMs without code.
Exploration is not distraction. These areas are chosen deliberately, based on where AI-native software is heading.
Yadnyesh Mulay, AI-first full-stack developer and business/AI consultant. I build useful software and advise on AI-driven business transformation. I turn ideas into working technical prototypes, and turn processes into AI & agentic AI roadmaps.
AI-first T-shaped Full Stack Developer & Business/AI Consultant, comfortable moving between product decisions, system architecture, the model layer itself, and the business process it all needs to serve. This is the record of how that came together.
Full-stack foundations
Shipped web applications end-to-end, React, Node.js, databases, deployment.
MSc Computer Science, University of Greenwich
Distinction. Capstone: Track-Master, a MERN train-ticket booking system with TfL API integration, PDF ticket generation, and payment processing.
Cloud & platform certification
AWS Solutions Architect – Associate, Azure Fundamentals (AZ-900), Azure Developer Associate (AZ-204), Google AI Essentials.
AI-native software
OutSkill AI Generalist Accelerator. Built Prism, the Microsoft 365 Sentiment Analyzer, and the Gmail/Chat Sentiment Analyzer, putting LLM APIs, RAG, and hybrid NLP into working products.
AI & business consulting
Advising on AI & agentic AI transformation roadmaps, technical product and program delivery, business process optimization, and no-code/low-code workflow automation (vibe coding).
AWS Solutions Architect – Associate
Amazon Web Services · 2024
Microsoft Azure Fundamentals (AZ-900)
Microsoft · 2023
Microsoft Azure Developer Associate (AZ-204)
Microsoft · 2024
Google AI Essentials
Google · 2024
OutSkill AI Generalist Accelerator
OutSkill · 2024
Skillship Foundation Aatmanirbhar Program, Best Performer, Python
Skillship Foundation · 2022
Community Manager
MahaWiki · 2022–Present
Open knowledge contribution and community coordination.
Community Manager
Cloud Community Group · May 2021 – Present
Cloud-native technologies, Kubernetes, serverless patterns.
Campus Club Lead
GESCOE MOZCLUB / Mozilla Campus Club · 2019–2021
Privacy/security advocacy, Firefox Add-ons development, HTML/WordPress workshops.
Firefox Beta Tester
Mozilla India, Quality Assurance · 2017
Volunteer QA contributor for Firefox Developer Edition test days (Preferences Search, CSS Grid Inspector, Form Autofill) and the Firefox Quantum website-compatibility testing push.
Campus Ambassador & Best Performer (Python)
SkillShip Foundation · 2020–2022
Ran student webinars and skill-building programs; recognised as Top Active Member at HackOnfest 2020 and Best Performer in the Aatmanirbhar Python track.
Next step
Start with the real idea.
Tell me what you're building or trying to learn. I'll tell you if I can help.
Let's build something useful.
I work with founders, engineering teams, and organizations building AI-native products and full-stack applications, and with business leaders shaping AI & agentic AI transformation roadmaps, whether that's a technical prototype, an AI integration, a process optimization engagement, or a full MVP.
Build
MVP Development · Full-Stack Applications · Web Applications · Technical Prototypes
Enhance Intelligence
LLM Integration · AI Automation · Agentic Workflows · Retrieval Systems (RAG)
Architect
System Design · Technology Selection · Scalability Planning · API Strategy
Prototype
Idea → Working Prototype · Feasibility Validation · Technical Spike · Proof of Concept
Product
UX + AI-Native Strategy · Product Discovery · Feature Prioritization · Metrics Design
Explore
Emerging Tech Feasibility · Local LLM Deployment · Agent Architecture · AI Infrastructure
Consult
AI & Agentic AI Transformation Roadmaps · Technical Product & Program Delivery · Business Process Optimization · No-Code / Low-Code Workflow Automation (Vibe Coding)
Form submissions are used only to schedule a conversation, not stored in databases, not shared, not used for marketing. No tracking pixels.
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Grab a 30-minute slot on my calendar.
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Pick a time
Opens my live calendar with available 30-minute slots.