Tangyi (Jerry) Qian

Architected and shipped the full-stack core (Python · FastAPI · SQLAlchemy 2.0 · NetworkX; React 18 · TypeScript · React Flow — 100+ REST endpoints over a 39-table versioned schema) modeling goals as typed directed multigraphs with live completion-state tracking and versioned snapshots.

Built the agent harness governing all mutations: a proposal-first review protocol (draft → diff preview → human commit/reject), a simulation sandbox for candidate plans, and a full event log capturing every action for auditability and downstream training data.

Built adaptive re-planning services that re-derive the critical path as reality changes (constraint-solver scheduling with Monte-Carlo feasibility checks); now building the conversational planner–executor agent: multi-step tool-call loops over CLI-wrapped system capabilities, bounded retries, and human-in-the-loop batch review.

Deployed a self-hosted 6-GPU inference node (vLLM serving GLM / MiniMax-class open models) for latency-critical agent steps: tuned tensor-parallel vs multi-replica layout and continuous batching, and exploited prefix caching over shared system-prompt and graph-state context across multi-turn sessions.

Built a dynamic model-routing layer trading off latency, cost, and capacity: routine agent steps (tool-argument formatting, summarization) run on the local node, complex planning steps escalate to frontier APIs, with load-aware spillover and per-step cost telemetry.

Engineered stateful agent-session serving: async long-running loops with streaming (WebSocket/SSE), checkpointed and resumable runs, idempotent tool mutations, per-user concurrency caps, and per-step tracing (model, tokens, latency) for production debuggability.

Migrated the platform to multi-tenant Postgres (39-table schema + full versioning system) with per-tenant isolation, graph-hydration caching keyed on (user, graph-version), and monthly event-log archival designed around training-data extraction.

Moved solver workloads off the request path onto a job queue with per-tenant quotas and incremental local-repair scheduling, keeping API latency flat under concurrent re-planning load.

Built the training-data flywheel: trajectory and preference records (proposal → accept / reject → actual outcome) extracted from the event log into parquet datasets, powering evaluation suites and a bandit layer for per-user proposal-style personalization.

Architected the end-to-end auto-research pipeline: ingestion and parsing of earnings releases, SEC filings, and market news (10K+ document corpus) into structured signals (guidance changes, event tags, sentiment deltas), with a gradient-boosted relevance scorer gating what enters the research queue.

Built the research agent core: given a ticker or event, the agent plans an evidence-gathering workflow (filing sections, price/volume context, peer comparisons, news timeline) compiled into a parallel tool-call DAG (LLMCompiler-style), producing citation-grounded research briefs.

Built a FrugalGPT / RouteLLM-style cascade router — a lightweight classifier over document type, relevance score, and small-model confidence routes routine coverage to a compact model and escalates only complex filings (M&A, restatements, non-standard disclosures) to a frontier model.