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Deven
Liscombe
Profile
Engineer building end-to-end AI systems across product, infrastructure, and agents.
Looking for an early-stage or AI-native team where I can own systems end-to-end — working closely with product and founders on ambiguous problems.
Readout
Focus
AI Systems
Infra + Product
Education
UWaterloo
BMath '25
Primary
TS / PY
Full Stack
Scope
End-to-End
Solo Builds
Experience
Founder / AI Systems Engineer
- 01Designed and built an end-to-end AI system for persistent context and memory in agent workflows — solving the core failure mode of modern AI: statelessness
- 02Implemented agent runtime, context graph (structured memory), orchestration layer, and MCP server as sole engineer
- 03Shipped consumer web app, desktop + mobile apps, and internal SDK across a 12-week build
Minto Group
2023Financial Analyst
- →Oversaw finances and cash flow projections for commercial real estate portfolio
- →Built VBA-automated budget templates, replacing manual workflows
Enwave
2022Corp Dev Finance Intern
- →Financial modelling for optimal pricing on historical electricity rate data
- →Rebuilt data archive into dashboard format for deal pricing
Education
University of Waterloo
BMath (Honours Mathematics)
2021 – 2025Majors in Mathematical Finance and Statistics.
Tech Stack
Deep Experience
Experience With
Featured Project
Concurrent
A system for persistent intelligence — a cognitive substrate that lets AI reason, remember, and act coherently across time, tools, and tasks. Solving the core failure mode of modern AI: statelessness.
System Architecture
What Shipped
- 01Consumer web app (primary)
- 02Marketing website
- 03Desktop + mobile apps
- 04Operator Studio (SDK + UI)
- 01Context Graph memory system
- 02Agent Orchestrator (ReAct)
- 03MCP Server integration
- 04Backend APIs (REST)
- 01PostgreSQL schemas
- 02Vector embeddings (semantic)
- 03Persistent session storage
- 04Memory-scoped inference
Other Projects
Quantitative Trading System
Python · PostgreSQL · Event Streaming · ML Pipelines
Full-stack trading infrastructure with clean separation between research, execution, and risk systems. Live trading consumes the same feature definitions as research — just computed in real-time.
Data Layer
- 01Raw → Normalized → Derived pipeline
- 02Timeseries store for bars/ticks
- 03Corporate actions + calendar
- 04Versioned dataset snapshots
Research + Strategy
- 01Event-driven backtest engine
- 02Realistic cost + slippage models
- 03Point-in-time ML training
- 04Model registry + versioning
Execution + Risk
- 01OMS with lifecycle state machine
- 02Pre/post-trade risk checks
- 03Position limits + drawdown stops
- 04Broker reconciliation service
Contact
Open to engineering roles at early-stage and AI-native teams. Prefer to work on ambiguous, high-leverage problems end-to-end.