I’m an AI Systems Engineer focused on building real-time, production-grade AI systems — especially around LLM workflows, structured intelligence, and stateful applications.

My core interest lies in systems where intelligence is not just generated, but controlled, structured, and made reliable under real-world constraints.

I also document how I think about systems and how I build stable, safe, and production-ready AI infrastructure — including architecture decisions, failure modes, and design tradeoffs.

I work across the full stack of modern AI systems:

• 🧾 Stateful AI applications with structured lifecycle management and conversational interfaces
• 🧠 LLM-based systems for reasoning, structuring, and controllable information extraction
• ⚙️ Low-latency AI workflows designed for reliability and real-world usage constraints

My focus is not just building AI features — but building systems that remain predictable, debuggable, and controllable even when inputs are ambiguous.

A local-first AI-ready job application tracking system with structured state management and conversational CRUD support.

It turns job searching into a stateful, queryable system instead of scattered manual tracking.

Focus areas:

• State machines for application lifecycle
• Natural language → structured CRUD operations
• Workflow tracking and observability

A system that represents my resume in a structured, modular format, allowing fine-grained control over what gets highlighted based on a given job description.

Instead of rewriting resumes blindly, it enables intent-driven selection of relevant projects and experiences from a structured internal representation.

Focus areas:

• Structured resume modeling
• JD-based matching and ranking
• Controllable information selection using LLMs
• Explainable filtering of projects/skills

👉 https://ai-engineer-portfolio-sigma-brown.vercel.app/

I’ll be expanding this portfolio with breakdowns of how I think about systems and how I design stable, safe, and production-ready AI systems over time.

I like building systems that are:

• 🧩 Stateful — every interaction has memory and lifecycle awareness
• ⚡ Reliable — designed to behave predictably under real-world conditions
• 🤖 AI-native — LLMs are embedded into system design, not bolted on
• 🔁 Composable — systems that can evolve into workflows and agents
• 🛡️ Controlled — outputs are structured, observable, and debuggable

I’m especially interested in how these systems behave when:

• inputs are ambiguous
• context is incomplete
• decisions must be explainable
• and failures are expected, not rare

• Memory-driven AI systems
• Structured LLM reasoning pipelines
• Multi-step agent orchestration
• Separation of generation, control, and execution in AI systems

• 🌐 Portfolio: https://ai-engineer-portfolio-sigma-brown.vercel.app/
• 💼 LinkedIn: https://www.linkedin.com/in/adityamore2k/
• 🐙 GitHub: http://github.com/adityadmore2000

"The future of AI systems is not just generation — it is structured control over intelligence itself."