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Cloud-Native GenAI Platform on AWS

Designed an end-to-end LLMOps platform with CI/CD, observability, and evaluation — a policy-aware control plane for routing enterprise AI workloads.

AWSEKSLLMOps Financial Services
40%
Infra Cost Saved
3x
Release Velocity
99.9%
Reliability
2
Provider Failover Paths

Business Problem

Teams were integrating model providers independently, creating duplicated security work, inconsistent observability, unpredictable costs, and no reliable fallback path when a model or provider degraded.

Architecture Overview

A single governed interface for model routing, quotas, prompt controls, PII handling, auditability, and provider failover — deployed as a cloud-native platform on AWS.

  1. 1 Application request
  2. 2 Identity + tenant policy
  3. 3 PII and prompt guard
  4. 4 Budget-aware model router
  5. 5 Provider adapters (Bedrock, OpenAI)
  6. 6 Trace, cost and audit pipeline

All components are deployed with private networking, scoped IAM roles, and encryption at rest and in transit.

Key Design Decisions

  • Policy before provider selection. Tenant entitlements, data classification, context limits, and budget are resolved before model ranking so an invalid route is never considered.
  • Failure-specific recovery paths. Rate limits, timeouts, content-policy failures, and capacity errors trigger different recovery paths instead of one generic retry loop.
  • Infrastructure as code end to end. Terraform-managed environments keep platform changes reviewable, repeatable, and promotable.

Technology Stack

AWS BedrockEKSTerraformPrometheusMLflowRedisOpenTelemetry

My Role

  • Architected the gateway control plane and routing layers.
  • Implemented tenant-scoped quotas, model allowlists, and PII masking.
  • Built provider adapters with circuit breakers and health scoring.
  • Set up CI/CD, observability dashboards, and cost attribution.

Measurable Impact

  • 40% Infra Cost Saved
  • 3x Release Velocity
  • 99.9% Reliability
  • 2 Provider Failover Paths

Challenges & How I Solved Them

  • Provider variance: A common contract across providers with explicit feature limits, instead of leaky abstractions.
  • Cost unpredictability: Budget-aware routing plus per-tenant caps made spend a controlled variable.
  • Silent degradation: Golden prompt suites and route-decision replay caught quality drift before users did.
  • Failover safety: Chaos tests on fallback paths validated behavior under provider outages.

Lessons Learned

  • A gateway is a policy engine first, a proxy second.
  • Model quality scores need continuous domain-specific calibration.
  • Idempotency keys and bounded retries beat aggressive retry loops.
  • Cost visibility changes engineering behavior more than cost limits.