AI Engineer specializing in RAG pipelines, LLM workflows, and full-stack SaaS development. Currently building real-world systems used in academic and product environments.
# context-aware retrieval over institutional knowledge
from langgraph.graph import StateGraph
from rag.retriever import HybridRetriever
graph = StateGraph(RAGState)
graph.add_node("retrieve", HybridRetriever(top_k=8))
graph.add_node("rerank", CrossEncoderReranker())
graph.add_node("answer", GroundedAnswerer(model="gpt-4o"))
# deterministic fallbacks → no hallucinated citations
app = graph.compile(checkpointer=PostgresSaver())
I work where AI, product engineering, and marketing systems overlap — building tools that move real metrics, not slide decks.
I design retrieval and reasoning systems that survive production — not demos.
End-to-end SaaS — from data model to deployed product.
Salesforce Marketing Cloud — beyond the standard activity catalogue.
Academic teams needed answers grounded in their own documents — syllabi, policies, archived research — without the hallucinations that off-the-shelf chatbots ship with.
Hybrid retriever (BM25 + dense embeddings) → cross-encoder rerank → grounded generation with citation enforcement. Deterministic fallback when confidence drops below threshold.
College project tracking was scattered across spreadsheets, email threads, and WhatsApp groups. Faculty couldn't see status; students couldn't see expectations.
Centralized workspace with AI-assisted progress summaries, milestone detection, and structured handoffs between students, mentors, and reviewers.
Aptitude prep tools optimize for question count, not mastery. Students grind without ever seeing where they're plateauing.
Topic graph + per-skill scoring, adaptive test sets, and a clean React + Django stack designed to scale beyond a single college.
Features are deliverables. Systems compound. Every component I ship should be reusable across two more contexts before it's done.
Code that another engineer can pick up at 2 a.m. always wins over a clever one-liner that requires me to explain it.
I use AI where it materially changes the outcome — not because the README mentions agents.
I work with founders and teams who need an engineer who can hold the full picture — from system design to the last line of production code.
See how I work →End-to-end design of LLM-powered features that ship to production users.
Drop reliable retrieval and reasoning into existing products without rewriting them.
React + Django product engineering — from idea to deployed v1.
Custom Journey Builder activities, AMPscript systems, and integrations.
Shipping BrainBench v1.2 with adaptive test sets.
AI-powered project workflows for ProjTrack Desk.
LLM orchestration patterns with LangGraph + state machines.
Whether you're hiring, exploring an AI product idea, or need a builder who can take it from zero to deployed — I'd like to hear about it.