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I'm an AI/ML Engineer interested in building AI systems that are useful beyond the demo.
My work sits at the intersection of LLMs, agentic systems, retrieval, automation, and software engineering. I enjoy taking an idea from an early prototype and turning it into something reliable, usable, and worth shipping.
I'm also a writer and lifelong learner. I share what I'm building, what I'm learning, and what I think is actually worth paying attention to in AI.
I like building things that make complex ideas feel simple.
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LLM applications, model orchestration, structured outputs, tool calling, and practical AI products. |
Stateful agents, tool use, multi agent workflows, MCP, planning, and human in the loop systems. |
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Retrieval pipelines, semantic search, document intelligence, codebase understanding, and knowledge intensive applications. |
Evaluation, observability, context engineering, APIs, deployment, reliability, and everything that happens after the prototype works. |
Tool Calling Structured Outputs Context Engineering Agent Evaluation
Evaluation Observability APIs Docker Automation
Proud to have been a Silver Winner at Evalathon for my work in the competition.
My GenAI project Study Pal was recognized and featured by the official Streamlit team on X.
An agentic research system designed to move beyond simple search and summarization.
The system explores how agents can plan research, use tools, gather information, evaluate sources, synthesize findings, and produce structured reports.
LangGraph MCP LLMs Python FastAPI
A RAG based application for understanding large codebases through natural language.
The focus is on code aware retrieval, repository navigation, contextual understanding, and useful developer workflows rather than simply indexing files and asking questions.
Python RAG FAISS LangChain FastAPI Docker
A growing collection of experiments around the parts of AI engineering that become important once the prototype works.
Agents MCP RAG Evals Observability Context Engineering LLM Routing
I write about AI engineering, learning, building, and the things I find interesting along the way.
A practical roadmap covering what to learn, what to build, and how to approach the AI engineering journey.
Published in Code Like A Girl
20 min read · July 2026
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A look at the tools and engineering practices involved in building production AI systems with Claude Code.
Published in The Tech Trek by Tech Chick
15 min read
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I write The Tech Trek, a newsletter about AI, software, engineering, and where technology is heading.
I try to keep it practical, curious, and free from unnecessary hype.
I'm always interested in meeting people who are building interesting things.
If you're working on an AI product, developer tool, open source project, research idea, or something that doesn't quite fit into a category yet, I'd love to hear about it.
I'm particularly interested in collaborations around:
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Building and shipping useful AI applications from idea to production. |
Agent frameworks, RAG systems, developer tools, AI infrastructure, and interesting experiments. |
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Agents, evaluation, retrieval, reasoning, model orchestration, and emerging AI engineering patterns. |
Writing, workshops, educational projects, and making difficult AI concepts easier to understand. |
Have an idea worth building?
Need someone to turn an AI concept into a working system?
Want to collaborate on something technically interesting?
Let's talk.
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I'm in my 20s, building with AI, sharing what I learn, and figuring things out as I go.
When I'm away from a terminal, I'm usually writing poetry, taking care of my plants, reading something interesting, or going down an unexpected internet rabbit hole.
Building AI, sharing what I learn, and making technology feel a little more human.
Portfolio · GitHub · X · Newsletter