AI Inference Unit Economics • Provable Agent Governance • Closed-Loop SRE • Hyperscale Cloud Platforms

Engineering executive with 25+ years leading global platform, cloud infrastructure, and AI systems organizations at Fortune-100 and hyperscale scope. Track record of owning multi-year technology roadmaps, managing $40M+ capital and compute budgets, and leading global organizations of 50+ engineers across NA, EMEA, and APAC through 5 engineering managers.

• Real-Time AI Inference at Scale (~1B Sessions/Month): Led platform engineering for an enterprise voice AI platform handling approximately one billion monthly sessions, executing the architectural transition from legacy RNN models to real-time LLM inference under a strict < 100 ms latency budget.
• Inference Unit Economics (~30% Cost Reduction): Architected complexity-based tiered model routing (routing high-volume deterministic intents to compact models and complex reasoning to frontier tiers with automated circuit-breaker fallbacks) and migrated self-hosted inference clusters to managed cloud AI services, reducing compute spend by ~30%.
• Hyperscale Fleet Modernization (10,000+ Nodes): Directed the migration of 10,000+ compute nodes to cloud-native Kubernetes architectures with automated multi-window OpenSLO error budget enforcement and zero-trust access boundaries.
• Provable AI Governance & FinOps Control Planes: Author of open-source enterprise reference architectures for deterministic agent execution governance (Autonomy Boundary Framework) and multi-tenant inference cost routing (DISPATCH).

flowchart TD
    Profile["Hooman Parta (@hoomanp)\nDirector of Engineering: AI Platform, Infrastructure & SRE"]

    subgraph Flagships["Core Enterprise Control Planes"]
        direction TB
        ABF["1. autonomy-boundary (ABF)\nProduction Guardrail & Ring 0 Runtime Control Plane\n• Approved == Authorized • <0.85ms PEP • 8/8 OWASP ASI • MCP Gateway"]
        Dispatch["2. dispatch (DISPATCH)\nEnterprise LLM Inference Gateway & FinOps Router\n• -80.7% workflow cost • 90% KV cache hit • Tier-0 quota preemption • <25ms failover"]
        KubeSRE["3. kube-sre-vantage\nClosed-Loop Kubernetes SRE & Zero-Trust Control Plane\n• Multi-window OpenSLO budgets • CPU/GPU pool isolation • 60s OIDC JIT grants"]
    end

    subgraph GovernanceTelemetry["Observability & Engineering Economics"]
        direction LR
        AdvObs["4. advanced-observability-sre\nService-Centric SLO Dependency & Blast Radius Engine\n• Dynamic OTel span graphs • CI/CD Stop-Ship gating"]
        Aether["5. aether-metrics\nExecutive AI Governance & Unit Economics Platform\n• DORA + Cost-per-Completed-Task • A2A test synthesis"]
    end

    Profile --> Flagships
    Flagships --> GovernanceTelemetry

• The Business Problem: Manual, retroactive compliance reviews and out-of-band configuration drift break enterprise delivery velocity, while autonomous agents executing database mutations, shell commands, or API transactions expose production infrastructure to unconstrained blast radius.
• The Architectural Invariant ("Approved Must Equal Authorized"): Enforces deterministic cryptographic binding between architectural intent and production execution. Canonical SHA-256 normalization, capability graphs, and Ed25519 signatures eliminate the gap between approved intent and runtime effect, invalidating stale grants fail-closed if live system state drifts prior to execution.
• The Economic & Risk Impact: Operates in < 0.85 ms with zero secondary LLM evaluation calls (100% marginal governance inference cost reduction), verifying 72 invariant test suites in 0.11s across all 8 OWASP Agentic Security Initiative (ASI) categories with drop-in CI/CD and Model Context Protocol (MCP) gateway adapters.

• The Business Problem: When enterprises scale from isolated AI pilots to 40+ engineering teams sharing foundation model endpoints, unstratified prompt headers invalidate transformer KV caches (+159% token spend), batch jobs exhaust shared TPM quotas causing Tier-0 customer brownouts, and agent tool-retry storms inflate cloud bills.
• The Architectural Invariant: Stratifies every agent payload into a deterministic 3-Block Prompt Contract (immutable sorted MCP tool schemas, HMAC-isolated session context, and tail-appended dynamic turns) paired with sticky SHA-256 worker routing, sub-25ms sliding-window circuit breakers, and hierarchical token buckets that preempt Tier-2 batch traffic at 80% global pool utilization.
• The Economic & Risk Impact: Reduces 10-step agent workflow cost by 80.7% ($1.080 to $0.209), sustains a 90.0% prefix cache hit ratio, and preserves 100% Tier-0 SLA availability under batch floods at 0.02 ms p99 gateway overhead.

• The Business Problem: Static threshold alerting drives on-call fatigue across multi-cloud fleets, while automated remediation operators routinely hold standing cluster-admin privileges that violate enterprise zero-trust security mandates.
• The Architectural Invariant: Decouples I/O-bound CPU control-plane orchestration from high-TCO GPU inference pools across EKS, AKS, and GKE. Correlates OpenTelemetry burn-rate alerts against ChromaDB vector memory of historical post-mortems and executes self-healing exclusively through a Zero-Trust Just-In-Time (JIT) access broker issuing scoped 60-second OIDC tokens.
• The Economic & Risk Impact: Eliminates standing privileged access across production clusters while preventing GPU fleet idling during external tool I/O and incident triage.

• The Business Problem: Static offline eval benchmarks fail to predict cascading microservice brownouts or runaway token churn introduced by canary releases and AI-assisted code generation.
• The Architectural Invariant: Reconstructs live service dependency multigraphs from OpenTelemetry span links to compute a mathematical Blast Radius Index ($BRI$) during canary rollouts, paired with executive DORA + Token-to-Value telemetry (Cost-per-Completed-Task, Prefix Cache Hit Ratio, and Cycle Time Delta).
• The Economic & Risk Impact: Enforces automated Stop-Ship CI/CD gates whenever $BRI \ge 1.0$ threatens Tier-0 error budgets, and reduces engineering cycle time by 24.8% via automated Agent-to-Agent (A2A) test coverage synthesis.

Author of the LinkedIn executive engineering series What Changed This Month and The Autonomy Boundary, examining production AI unit economics, infrastructure trade-offs, and deterministic runtime governance:

1. LLM Unit Economics & Prompt Caching: Why headline token list prices no longer dictate production AI bills: modeling prefix-cache reuse (61%–80% task cost reduction), cache-write surcharges, TTL eviction boundaries, and complexity-based tier routing.
2. RAG Economics & Retrieval Precision: Evaluating the unit cost and recall degradation of 1M-token context windows versus hybrid vector retrieval, cross-encoder reranking, and abstention-trained models.
3. Eval-Driven Deployment & Stop-Ship Gates: Why static offline benchmarks fail in production: implementing synthetic canary evaluations, Blast Radius Index ($BRI$) gating, and Ring 0 execution boundaries.
4. Decoupling Agent Orchestration from GPU Inference: Unbundling I/O-bound agent tool loops onto elastic CPU nodes while isolating high-TCO GPU clusters strictly for forward passes and high-utilization MoE serving.

• LinkedIn: linkedin.com/in/hooman-parta
• GitHub: github.com/hoomanp
• Location: Los Angeles, CA / Remote (Global Executive Scope)