2020
Full-stack foundation
Delivered core product surfaces across frontend, backend, and cloud deployment pipelines.
Technical insight: Established API versioning, auth boundaries, and release discipline early.
01Production AI Systems Engineer
Building Production-Grade AI Systems That Scale.
AI Full-Stack Engineer with 5+ years building scalable SaaS platforms, intelligent workflows, and real-world LLM systems.
14+ production systems99.95% uptime targets3M+ end-user interactions
preparing spline scene
Journey
From Shipping Features to Designing Systems
My path moved from full-stack delivery to architecture decisions that keep AI products reliable under real production pressure.
2021
Scaling pressure
Resolved growth bottlenecks in queueing, caching, and database hotspots.
Technical insight: Introduced async job pipelines and observability-first debugging.
022022
AI transition
Shifted from static automation to model-assisted product workflows.
Technical insight: Built retrieval and classification systems with measurable quality loops.
032023
Production reliability
Reduced model incidents and response variance in customer-facing systems.
Technical insight: Added eval harnesses, guardrails, retries, and structured fallback paths.
042024-2026
LLM pipelines and architecture
Designed multi-tenant AI systems balancing latency, quality, and cost under real usage.
Technical insight: Focused on retrieval quality, orchestration patterns, and failure isolation.
05Projects
Selected AI Systems
Each case study reflects architectural trade-offs made under real latency, reliability, and cost constraints.
Case Study 01
Support Copilot for B2B SaaS
A production assistant for support teams that handles retrieval, drafting, and confidence-driven escalation.
Problem: Support teams were overloaded with inconsistent answer quality and slow first responses.
Scalability solved: Implemented multi-tenant vector namespaces and queue-based inference load leveling.
Latency optimization: Parallel retrieval plus streamed responses reduced p95 latency by 38%.
Cost strategy: Prompt compression and model routing cut inference costs by 34%.
Median Response Time
-0%
CSAT
+0%
Assisted Resolution
0%
Key Architectural Decisions
- Hybrid retrieval with confidence scoring
- Tenant-level prompt isolation
- Human-in-loop fallback for low confidence
Next.jsFastAPIPostgreSQLpgvectorRedisKafkaAWS
Case Study 02
Revenue Intelligence Engine
An event-driven AI layer that turns fragmented CRM and call data into reliable deal intelligence.
Problem: Critical deal insights were buried across calls, CRM updates, and support activity.
Scalability solved: Designed streaming ETL with replay-safe backfills for high-volume event recovery.
Latency optimization: Precomputed embeddings for hot entities lowered query wait time by 44%.
Cost strategy: Batch summarization windows and stable-context caching reduced token spend.
Insight Coverage
0x
Win-rate Influence
+0%
Manual Analysis Time
-0%
Key Architectural Decisions
- Event ingestion bus with idempotent processors
- Entity linking across fragmented signals
- RAG summarization with confidence tags
Node.jsPythonTemporalBigQueryPineconedbtGCP
Case Study 03
AI Workflow Orchestrator
A state-machine platform for high-volume AI workflows with governance, retries, and auditability.
Problem: Operations workflows were fragmented, brittle, and expensive to maintain at scale.
Scalability solved: Isolated worker pools by tenant criticality and SLA class to prevent noisy neighbors.
Latency optimization: Warm model contexts for repeated tasks reduced average step duration by 31%.
Cost strategy: Dynamic model tiering aligned model spend with business impact.
Daily Workflow Runs
0k+
Reliability Target
0%
Ops Overhead
-0%
Key Architectural Decisions
- State-machine orchestration with explicit recovery paths
- Tool-call governance and policy boundaries
- Per-tenant audit trails
TypeScriptGoTemporalKubernetesClickHouseVault
AI Systems
How I Design AI Systems
Architecture is treated as a set of explicit trade-offs between quality, speed, reliability, and cost.
RAG Architecture
I treat retrieval as a ranking problem, not a database lookup. Query rewriting, chunk strategy, and metadata discipline often drive quality more than prompt tuning.
Query
Trade-off: Strict normalization improves retrieval consistency but can reduce user nuance.
Latency impact: +15 to +40ms
Failure mode: Intent drift during rewrite.
Agent Systems
Agents are useful when task decomposition and tool orchestration outperform deterministic pipelines. I constrain them with policy boundaries and explicit handoff states.
Evaluation Framework
Eval loops combine offline benchmarks with production traces, then feed regression gates before release.
Hallucination rate reduced from 11.8% to 3.2% across six evaluation iterations.
Cost Optimization
Model quality should be routed by business impact, not by defaulting to the largest model.
- Model Tier
- Medium
- Expected Quality
- 81%
- Est. Cost / 1K
- $3.32
Stack
Stack I Trust in Production
Tools are selected based on operational reliability, observability, and long-term maintainability under production load.
Experience
Built for Production, Not Demos
Architecture choices are measured by reliability, cost efficiency, and user outcomes.
Years Experience
0+
Production Systems
0+
Latency Reduction
0%
End-User Interactions
0M+
Writing
Writing on AI Systems
Thought pieces focused on architecture trade-offs, operational reliability, and evaluation discipline.
Contact
Let's Build Intelligent Systems That Actually Work.
If you're scaling an AI product and need architecture that survives production reality, let's talk.