AI / Cloud / Distributed Systems

Lennon Lin

Staff-level Software Engineer / AI Platform & Cloud Architect

Building production LLM platforms, edge AI systems, and distributed data infrastructure from prototype to enterprise deployment.

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Profile Focus

  • A2A agent frameworks and multi-LLM routing
  • RAG systems validated with FRAMES and Ragas
  • Low-latency C++ inference on edge devices
  • Spark / Hadoop telemetry pipelines at scale

About

Engineering leadership for intelligent systems.

I am a senior software engineer with 10+ years building production AI, computer-vision, and distributed systems end-to-end, from low-latency C++ edge inference engines to cloud-native LLM platforms.

My work sits at the intersection of AI platform architecture, RAG systems, edge-cloud deployment, and large-scale data pipelines on GCP and AWS, with repeated ownership from prototype through commercialized enterprise deployment.

Expertise

Systems experience across AI, cloud, and data.

LLM / Agent Platforms

  • A2A framework design
  • Multi-hop reasoning agents
  • RAG
  • Vertex AI / Azure OpenAI

Cloud Architecture

  • GCP Cloud Run / Build / GCE
  • AWS EC2 / S3 / IAM
  • Docker
  • Event-driven microservices

Distributed Data

  • Spark
  • Hadoop / MapReduce
  • ETL pipelines
  • Telemetry ingestion

Edge AI / Vision

  • C++
  • TensorRT / OpenVINO
  • NVIDIA Jetson
  • YOLO / SSD / FPN

Experience

A continuous arc from edge perception to LLM platforms.

  1. Jan 2023 — Present

    Associate Manager / Cloud Architect

    Acer — Advanced Tech BU

    Lead Architect for the cloud-native AI Agent Platform on GCP. Designed a modular Agent-to-Agent (A2A) framework with dynamic runtime loading, multi-agent orchestration, real-time ASR, and large-scale RAG retrieval.

    • A2A framework + multi-LLM routing (GPT-4 Turbo / Claude 3.5 / Gemini 2.0)
    • FRAMES + Ragas RAG validation across 3,131 human-verified QA pairs
    • Led cross-functional teams (Backend, ML, ASR) and SI partners through enterprise rollouts

  2. Jun 2019 — Jan 2023

    Technical Lead & Cloud System Architect

    Acer — Advanced Tech BU

    Owned end-to-end engineering of a commercial edge-to-cloud AI platform deployed across retail and transportation. Built a real-time C++ inference engine with Cython/Python integration achieving sub-second latency in production.

    • Co-led Taipei Metro Face-Recognition Gate proof of concept
    • n:n face recognition platform — 97.24% MegaFace, 27 devices, 4,324 identities
    • Distributed edge–cloud hybrid platform integrating embedded devices, CV sensors, and cloud microservices

  3. Feb 2017 — Jun 2019

    Deep Learning Tech Lead

    Acer — Advanced Tech BU

    Simulation-driven model development. Built a virtual-to-physical feedback loop pairing GTA-V environments with real-time shared-memory inference for autonomous-driving perception and control.

    • ResNet-50 multi-task regression for steering-angle prediction
    • Models deployed to golf carts and mini autonomous vehicles
    • Segmentation, detection, and perception modules across the evaluation pipeline

Selected Work

Production systems with measurable impact.

01

AI Agent Platform

A2A framework / RAG / multi-LLM routing

Architected a cloud-native AI platform on GCP with dynamic runtime agent loading, multi-agent orchestration, and multi-LLM routing across GPT-4 Turbo, Claude 3.5 Sonnet, and Gemini 2.0 Flash.

  • RAG knowledge base scaled to 2.4M Chinese characters
  • FRAMES / Ragas validation with 3,131 human-verified QA pairs
  • Hybrid deployment across RTX 4090 servers and cloud LLM APIs

02

Multilingual Technical Translation Agent

LLM workflow automation

Delivered a multilingual technical-translation agent with a four-stage verification pipeline covering exact match, AI review, generalization, and human review.

  • 29 languages supported
  • 99.9% accuracy on familiar specifications
  • 120K rows processed in about 2 days across 10 API workers

03

Edge AI Face Recognition Platform

Commercial edge-to-cloud computer vision

Led architecture and engineering of a commercial n:n face-recognition platform with real-time C++ inference, edge hardware optimization, and cloud verification services.

  • 97.24% MegaFace benchmark accuracy
  • 27 production devices and 4,324 enrolled identities
  • Sub-1-second recognition latency in real environments

04

Distributed Telemetry Platform

Spark / Hadoop analytics infrastructure

Re-architected global telemetry ingestion and preprocessing systems using Spark and Hadoop MapReduce for device analytics and OTA workflows.

  • ETL runtime reduced from 24 hours to 2 hours
  • 300K-600K device packages ingested per day
  • About 50M-90M CSV rows processed daily

Career Highlights

Evidence of scale, ownership, and delivery.

10+ years in software engineering

Built AI products from prototype to commercial deployment

Validated RAG quality with 3,131 human-verified QA pairs

Delivered 29-language translation automation at 99.9% accuracy

Achieved 97.24% MegaFace accuracy for edge face recognition

Reduced ETL runtime from 24 hours to 2 hours

Processed about 50M-90M telemetry CSV rows per day

Contact

Available for Staff-level AI platform and applied ML infrastructure roles.

Seeking Staff / Principal-level individual-contributor opportunities in AI platform, applied ML infrastructure, LLM applications, cloud architecture, and senior software engineering.

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