Shea Winkler

Data Architect | AI Systems | Distributed Platforms

I design and build production systems across AI infrastructure, data platforms, and real-time decision pipelines.

Denver, CO (Remote) · contextlattice.io · LinkedIn

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Brief

• Building production systems across AI infrastructure, data platforms, and real-time decision pipelines.
• Primary systems: ContextLattice, Hermes Agent Ultra, AlgoTrader (Rust), and FastAPI Sidecar.
• Current focus: runtime reliability, recall quality, context compression, and execution quality.
• Long-horizon, outcome-driven AI decision-making and integrity engineering.

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🚀 System Dossiers

🧠 ContextLattice

Local-first memory and context orchestration infrastructure for AI agents.

• HTTP-native gateway for tools, models, and retrieval
• Multi-service control plane implemented in Go and Rust
• Grounded context packs fused from vector, graph, and rollup evidence
• Ongoing focus on recall quality, context compression, and runtime reliability

👉 ContextLattice

🪽 Hermes Agent Ultra

Rust-first autonomous agent runtime focused on reliability, policy control, and upstream parity management.

• Rust-native runtime for agent loop, tools, gateway, skills, and CLI/TUI
• Deterministic policy controls for tool execution (audit, simulate, enforce)
• Upstream parity gates with replayable diagnostics for long-running sessions

👉 hermes-agent-ultra

⚡ AlgoTrader (Rust)

Real-time decision and execution system focused on runtime behavior, constraints, and feedback loops.

• Event-driven execution engine with continuous strategy and risk tuning
• FastAPI Sidecar integration for model routing, control signals, and telemetry
• Ongoing focus on execution quality, slippage control, and runtime behavior

👉 algotrader-community

🔌 FastAPI Sidecar

Service layer for orchestration between models and execution systems.

• API control plane for inference, telemetry, and execution control
• Model orchestration and cross-service coordination for live systems
• Bridge layer between Rust execution engines and Python model workflows

👉 fastapi-sidecar

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🧱 Systems Focus

• Distributed systems architecture across AWS, GCP, and Kubernetes
• Large-scale data platform design with BigQuery
• Geospatial pipeline engineering (joins, indexing, performance)
• Event-driven, low-latency runtime design
• AI infrastructure across memory, retrieval, and orchestration layers
• Cross-runtime integration across Rust, Python, and Go services

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🧭 Current Work

Current Work Operating ContextLattice as a memory control plane that composes grounded context packs from vector, graph, and rollup evidence for agent decisions Advancing Hermes Agent Ultra as a policy-governed autonomous runtime with deterministic tool controls, parity gates, and incident-grade observability Driving AlgoTrader + FastAPI Sidecar as a closed-loop execution platform coupling model routing, telemetry, and risk-aware trade orchestration

Current Priorities Define and enforce SLOs for retrieval latency, recall quality, and degradation behavior across memory lanes Ship reliability-by-default agent operations with enforce-mode policies, replayable traces, and runbooked incident response Tighten signal-to-execution feedback loops across Rust engines and Python/Go sidecars under live runtime constraints

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