AI & Automation Engineer

AI / Automation Engineer

Practicing AI + automation with transparent limits

I have not launched AI copilots for paying customers. This page documents AI and automation prototypes I have built while learning with ChatGPT, Copilot, and local LLMs.

What I have

Local AI experiments that run on my personal development machine.
Documented prompts, edits, and limitations in READMEs.
Basic automation scripts tied to personal projects.

What I don’t have yet

Production AI integrations or real-user telemetry pipelines.
Enterprise guardrails, audits, or policy enforcement.
Large-scale orchestration across teams and systems.

What I’m doing next

Better evaluation workflows for outputs and hallucinations.
Richer prompt orchestration with queues + storage.
Security and privacy reviews before public releases.

Local-first experiments

Convo-AI runs a FastAPI backend with Ollama locally for chat workflows.
No services are hosted for external users; all workloads run locally.

Documentation + prompts

Prompt libraries and README logs documenting which content was AI-drafted and which content was manually edited.
No production integrations or enterprise telemetry; these are learning projects with documented TODOs.

Convo-AI

FastAPI backend + simple UI for local chat flows.
Uses Ollama models and environment variables documented in the repo.
Disclosure: AI drafted the initial version of most endpoints; prompts and subsequent edits are documented in the README.

Proof links: Convo-AI

AI workflow notes

Documented how I structure AI-assisted builds and where I still rely on manual checks.
Focus is on transparency regarding what AI drafted and what was rewritten manually.
Used to keep expectations realistic for collaborators.

Proof links: AI workflow post

Python + FastAPI — learningNode.js / Express — prototypesLangChain — exploringOllama + local LLMs — local onlyOpenAI / Anthropic APIs — experimentsSupabase — exploring vector storesGitHub Actions — small deploys

Each repository labels working features versus experimental or aspirational features to indicate maturity level.

Responsible AI guardrails (policy checks, escalation paths) in production environments.
Measuring ROI beyond “this feels faster on my laptop.”
Scaling prompt orchestration with queues, storage, and audit requirements.
Security/privacy reviews for AI features before they reach real users.

If you mentor junior engineers on applied AI or automation, I am open to pairing sessions.

Practicing AI + automation with transparent limits

Clear scope, upfront

What I have

What I don’t have yet

What I’m doing next

Current focus

Local-first experiments

Documentation + prompts

Proof on GitHub

Convo-AI

AI workflow notes

What I’m experimenting with

What I still need to learn