Seeking AI/ML engineering internships and entry-level roles. I build agent systems, analytics pipelines, and engineering automation with a focus on reliability and practical use.
Selected Work
Flagship Projects
Four flagship AI systems.
Analytics Agents
DataInsight
AI-powered data analysis platform that lets non-technical users explore messy business data through natural language, structured planning, and adaptive analysis pipelines.
- LLM orchestration with agentic planning and replanning
- Automated cleaning, analysis, and response generation pipeline
- Clarification loops and fault-tolerant handling for imperfect data
Agent Systems
Alden
Private local-first planning assistant built around bounded autonomy, tool orchestration, stateful memory, and reliable execution under constraints.
- Multi-step planning and tool routing
- SQL-backed state and preference memory
- Human-in-the-loop control over automation
Automation and Agents
Budget Agent
Telegram-based personal finance agent that turns receipts, transaction files, and budget questions into structured records, analysis, and forward-looking repayment planning.
- OCR and CSV intake for messy financial inputs
- SQLite-backed records with specialist-agent routing
- Forecasting that supported a repayment path projected to save about $100 per month
Personal Assistant Systems
MIRA
OpenClaw-style personal assistant built from scratch with its own routing, memory, scheduling, and multi-channel communication architecture.
- FastAPI IO hub paired with a Telegram interface
- Custom router, dispatcher, and scheduler components
- SQLite-backed long-term memory with semantic search support
Background
Technical focus with strong psychology knowledge
Applied AI engineering
I focus on building applied AI systems, data workflows, and automation that can move from prototype toward production-quality execution.
Human-centered systems thinking
My Industrial-Organizational Psychology background helps me choose better problems, evaluate usefulness carefully, and design systems that fit how people actually work.
Clear constraints and tradeoffs
I care about what works today, what still needs validation, and which engineering tradeoffs matter most.
Strengths
Core Strengths
Applied AI
- LLM workflows, agent patterns, routing, and tool use
- Structured planning, analytics orchestration, and adaptive pipelines
- Retrieval systems, stateful memory, and workflow-aware assistants
Engineering
- Python system building, backend logic, and automation
- State management, documentation, and repository hygiene
- Architecture decisions shaped by reliability and clarity
Evaluation
- Error recovery, workflow iteration, and reliability-focused design
- Structured thinking about human feedback and system usefulness
- Research-informed framing without overclaiming maturity
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