Senior Technical Leader focused on Kubernetes-based distributed systems, workload isolation patterns, and AI-operationalized infrastructure in regulated enterprise environments.
I design systems that are:
- Scalable under burst load
- Governed under compliance constraints
- Deterministic under failure
- AI-augmented without sacrificing control
- Observable, diagnosable, and self-healing
- Kubernetes workload isolation & intelligent routing (light vs heavy patterns)
- Autoscaling strategy (HPA + resource governance)
- Kafka-based decoupled architectures
- Distributed system resilience & failure isolation
- Observability-driven scaling decisions
- Production-grade AI orchestration patterns (governed agent workflows)
- Enterprise-safe AI operationalization (approval gates, policy guardrails, audit trails)
- Multi-agent reasoning systems with deterministic aggregation logic
- Provider-resilient data ingestion architectures
- Conviction × Risk Gate decision governance frameworks
Advanced multi-agent AI system that formalizes high-conviction, high-beta investing into a structured, deterministic governance framework.
Stock-Agent is not a trading bot.
It is an accountability engine that separates:
What we want to do (Conviction)
from
What we are allowed to do (Risk Gate)
Each position is evaluated by specialized agents:
- Thesis Integrity Agent
- Risk Exposure Agent
- Sentiment Agent
- Skeptic (Adversarial) Agent
- Bias Detection Agent
- Consistency Agent
- Judge Aggregation Agent
Agent outputs are aggregated deterministically to ensure repeatable, explainable decisions.
Produces:
- Conviction stance (ADD / HOLD / TRIM / SELL REVIEW)
- Conviction strength (0–100)
Conviction score incorporates:
- Thesis integrity
- Catalyst momentum
- Sentiment alignment
- Predictive support
- Risk exposure penalty
- Skeptic pressure penalty
- Disagreement index
- Instability / jitter penalty
Conviction reflects directional belief strength only.
Applies deterministic portfolio governance:
- Allocation vs target max
- SellRisk overrides
- Regime tightening (risk_on / risk_off)
- Flow headwinds (momentum + volume)
- Predictive headwinds
- Portfolio concentration constraints
Default gating thresholds:
- ADD ≥ 85
- ADD SMALL ≥ 70
- HOLD ≥ 55
- TRIM ≥ 40
In risk_off:
- ADD is capped to ADD SMALL
Over target max:
- ADD is blocked
SellRisk ≥ 70:
- Forced SELL REVIEW
Quant is used for structural awareness — not blind prediction.
Includes:
- Beta vs SPY
- Annualized volatility
- 12–1 momentum
- Correlation clustering
- HHI concentration
- Historical VaR & Expected Shortfall
- Monte Carlo drawdown simulation
- Liquidity classification
- Volume regime detection
Short-horizon positioning state derived from:
- 5-day momentum
- 20-day momentum
- 20-day annualized volatility
- Volume ratio
Flow can block or cap adds.
Short-horizon ridge regression model using:
- Multi-horizon momentum
- Volatility features
- Volume ratio
Outputs:
- Direction (BULLISH / NEUTRAL / BEARISH)
- Expected return
- Probability positive
- Confidence score
Predictive signals influence Conviction and Risk Gate penalties but never override governance alone.
Each run generates:
- Conv column (stance + strength)
- Gated Effective Score
- Explicit Gate Blocks (e.g., OVER_TARGET, SELLRISK_ELEVATED)
- Short-horizon predictive metrics
- Data coverage indicators
The report includes an updated Efficacy Over Time chart tracking:
- Hit rate
- Average forward return
- Action-conditioned returns
- Cumulative performance
This makes the system measurable, auditable, and continuously evaluated.
Primary: Yahoo Finance (yfinance)
Fallback: Stooq daily CSV endpoint
Runtime behavior:
- Check cache
- Attempt Yahoo fetch
- If empty/blocked → fallback to Stooq
- If both fail → mark data unavailable explicitly
Each ticker includes a Data Coverage Indicator to prevent silent gaps.
Repo: https://github.com/Bellbotics/stock-agent
Enterprise-grade AI orchestration framework demonstrating safe operationalization of LLM-driven workflows in regulated environments.
Capabilities:
- Stateful graph-based orchestration
- Deterministic node transitions
- Human-in-the-loop approval gates
- Policy-as-code enforcement
- Artifact-driven execution
- Persistent audit trails
- Resumable workflow execution
Repo: https://github.com/Bellbotics/enterprise-langgraph-ops-demo
Reference architecture demonstrating:
- Kafka-based workload decoupling
- Intelligent routing (light vs heavy classification)
- Independent worker pools with separate HPAs
- CPU-driven scaling under burst conditions
- Isolation preventing cross-workload interference
Repo: https://github.com/Bellbotics/k8s-intelligent-workload-orchestrator
Kubernetes-deployed ML sidecar forecasting memory usage for compute-intensive document workloads.
Prevents OOMKills and improves routing efficiency under burst conditions.
Repo: https://github.com/Bellbotics/memory-spike-predictor
- Operate Kubernetes ecosystems supporting millions of API calls per day
- Process ~18K+ daily compute-intensive document transactions
- Tune pod CPU/memory constraints under production load
- Implement service mesh resilience policies (Istio)
- Instrument distributed systems with Datadog & OpenTelemetry
- Modernize legacy integrations to event-driven architectures
- Build governance-first AI orchestration systems
- Design provider-resilient ingestion layers
- Engineer deterministic aggregation across multi-agent systems
- Lead architecture sessions with Deloitte GPS CTO leadership
- Translate compliance requirements into scalable platform designs
- Bridge executive strategy and distributed system implementation
- Mentor engineers in reliability, scaling, AI safety, and cloud-native best practices
- Advocate deterministic AI orchestration over black-box automation
Resume: resume/greg-bell-resume.md
LinkedIn: https://www.linkedin.com/in/gregwbell/
All projects listed are architectural demonstrations.
They are not financial advice or endorsements of any specific technology.