AI × SYSTEMS × GAMES × QUANTUM
I don't just want to use the future. I want to understand how to build it.
I'm an AI Engineer / Consultant at PharmaEdge, building AI systems for pharmaceutical intelligence: news and clinical intelligence, information extraction, summarisation and semantic search, running in production.
Outside work I fix things upstream in the tools I depend on: AWS SDKs, LLM routing, agent benchmarks and game netcode. Before AI, I built games in Unity and Unreal, and that still shapes how I think about systems.
I like taking things from idea → experiment → architecture → implementation → production, and I like knowing why they work once they get there.
Pull requests to projects I don't own. Refreshed daily by a GitHub Action from the GitHub API; status badges are live.
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Production AI, not demos.
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The engineering around the model.
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Things that don't fit a job title.
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Going deeper, slowly and properly.
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| AI | ML Deep Learning LLMs GenAI RAG NLP Agents |
| Engineering | |
| Languages | |
| Games | |
| Infrastructure | Traefik GitHub Actions GitLab CI/CD |
A model is only one component.
USER ──▶ API ──▶ ORCHESTRATION
│
┌───────────────────┼───────────────────┐
▼ ▼ ▼
RETRIEVAL TOOLS MEMORY
└───────────────────┼───────────────────┘
▼
MODEL ──▶ VALIDATION ──▶ STRUCTURED OUTPUT
The interesting engineering starts after the model responds: reliability, observability, security, evaluation, fallbacks and failure handling are what turn an AI demo into a system.
My day job sits where pharmaceutical data meets LLMs, retrieval and automation.
News intelligence Clinical intelligence Semantic analysis Information extraction Summarisation Clustering AI-assisted research Production AI services
The goal isn't "generate a summary". It's:
SOURCE ──▶ EVIDENCE ──▶ UNDERSTANDING ──▶ STRUCTURED INTELLIGENCE ──▶ HUMAN DECISION
GAME DEV ──▶ SOFTWARE ENGINEERING ──▶ AI / ML / LLMs ──▶ PRODUCTION SYSTEMS ──▶ QUANTUM
Unity/Unreal Backend / APIs NLP / LLMs Agents / RAG / Data
Games taught me systems, interaction and user experience. Backend engineering taught me reliability and architecture. AI taught me uncertainty. I'm interested in what happens when all three meet.
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An independent space for building and experimenting. AI · Software · Systems · Experiments |
Games, interactive systems and the tech behind them. Unity · Unreal · C# · C++ |
| PharmaEdge | AI Engineer / Consultant | AI-driven pharmaceutical intelligence and production systems |
| Trucube Infotech | Full-Stack · AI · Game · Blockchain | AI/ML Backend Full-Stack Unity Unreal C# C++ Blockchain |
| UPES | M.Tech, Computer Science & Engineering | Artificial Intelligence / Machine Learning |
| UPES | B.Tech, Computer Science & Engineering | Graphics and Gaming |
- Understand before optimizing. If I don't know where the bottleneck is, I'm guessing.
- A prototype isn't production. Production means
security · failures · observability · scalability · maintenance. - Simplicity wins. Complexity should solve a problem, not create one.
- Build things you can explain. If I can't explain why a component exists, it deserves another look.
- AI needs engineering. Good prompts help. Good systems matter more.
Python → DSA → Mathematics → CS Foundations → Advanced AI Systems → Quantum Information → Quantum Algorithms
Not just "how do I use it?" but "why does it work, why does it fail, what happens underneath, and can I build it myself?" That curiosity is behind most of what's in my repositories.
