Skills

Key Skills

Data Engineering Machine Learning Methodologies Optimization Techniques Predictive & Statistical Modelling Sentiment Analysis Data Analysis LangChain LangGraph Multi-Actor Agents Agent Checkpointing Data Visualization Project Delivery Programming Process Improvement RAG Team Coordination & Leadership Debugging Adaptability Resourcefulness

Technical Skills

Languages & Frameworks: Python, LangGraph, LangChain, HTML
Cloud Computing: AWS, S3
Machine/Deep Learning: ANN, CNN, LSTM
Database: PostgreSQL (pgvector), SQLite, MySQL, MongoDB
Statistics/ML: Linear/Logistic Regression, Ensemble Trees, Gradient Boosted Trees

LangGraph Multi-Actor Agent Engineering

I architected and built the LangGraph Playground Monorepo, a modular engineering workspace demonstrating production-ready stateful agent patterns:

Stateful Cyclic Workflows & Reducers: Designed core state schemas (BaseAgentState, SQLAgentState) utilizing message reducers (add_messages) for continuous context passing.
Conditional Routing & Tool-Calling: Built dynamic agents with multi-branch routing between model inference and custom tools (calculator, SQL utilities).
Session Checkpointing & Memory: Integrated MemorySaver checkpointers for persistent multi-turn conversational agents.
Self-Healing SQL Generation: Engineered guardrailed agents that autonomously catch SQL execution errors, retry with corrected syntax, and block destructive operations (FORBIDDEN_SQL_KEYWORDS).

View GitHub Repository →

ETL Pipeline Skill Suite

I designed a set of reusable, composable AI agent skills that encode an entire data team’s pipeline conventions — directory structure, coding standards, and validation rules — so scaffolding a new data pipeline or auditing an existing one follows a consistent, self-checking process instead of relying on tribal knowledge.

What it does: I route each request — build a new pipeline, fix an existing one, or extend one with a new data category — to the right specialized skill. Each skill scaffolds or audits the standard pipeline stages — data collection, cleaning/transformation, and output — including a genuinely detailed document-extraction path I built that handles both text-based and image/scanned document sources, with automatic page detection and OCR fallback. I added a companion skill that generates and audits data-validation schemas, and another that generates the pipeline’s run configuration (schedule and step dependencies). I gave every skill the same built-in safeguards: it won’t write changes on a protected branch without confirmation, it supports both a guided step-by-step mode and a fully automated mode, and it lets me navigate backward to revise an earlier decision without losing downstream work.

Why it matters: I shifted pipeline scaffolding from “an engineer manually follows a checklist” to “an agent enforces the checklist automatically,” while keeping a human in the loop for any decision that touches production data or a shared branch.