About

About Antonio Osorio

I help startups and enterprise teams build software that ships — AI systems, data-intensive backends, DevOps and continuous delivery, and the engineering infrastructure to scale. 20+ years of hands-on experience at Netflix, Amplitude, and Schrödinger.

Why Work With Me

Enterprise Patterns Without Enterprise Bureaucracy

I’ve spent 20+ years building software at scale — owning client data infrastructure at Netflix (~1–2% of global internet upload traffic), leading cloud engineering and scaling at Amplitude, and building the distributed execution platform at Schrödinger that powers pharmaceutical drug design. I know what production-grade looks like, and I know how to move fast.

Clear Thinking, No Fluff

I know how to separate signal from noise — which AI investments pay off and which are expensive distractions. You get structured analysis focused on business outcomes, not buzzwords.

Hands-On Building, Not Just Slides

I write code. I review PRs. I debug production issues at 2am when needed. Strategy matters, but so does execution. You get someone who can think at the architecture level and also get things working.

Background

The Path to AI Consulting

At Netflix, I owned the infrastructure that ingested all data sent by client devices — roughly 1–2% of global internet upload traffic. I was responsible for ensuring that data was properly annotated, categorized, and prepared for analytics and AI applications, including high-stakes datasets like User Viewing History at scale.

At Amplitude, I led Cloud Engineering, DevOps, and Security & Compliance. I led the team that scaled Amplitude’s infrastructure, drove SOC2 certification, and owned developer productivity — shipping CI/CD systems, blameless post-mortem culture, and tooling that meaningfully increased engineering velocity.

At Schrödinger, I owned the distributed execution platform that ran physics-based models — map/reduce operations at scale — to predict the efficiency of potential drug compounds. The service I built was a core part of the tool pharmaceutical companies use to design new drugs.

Now I help companies apply these lessons to AI — building systems that are not just technically impressive, but actually useful and reliable.

Areas of Focus

AI Systems

  • LLM integrations and prompt engineering
  • Retrieval-Augmented Generation (RAG) pipelines
  • Data annotation, categorization, and AI readiness
  • Evaluation, monitoring, and safety guardrails

Data-Intensive Systems at Scale

  • High-throughput data pipelines and event processing
  • Distributed systems and backend architecture
  • Analytics infrastructure and data warehousing
  • APIs and microservices at scale

DevOps & Continuous Delivery

  • CI/CD pipelines and release automation
  • Cloud infrastructure (AWS, GCP)
  • Security, compliance, and SOC2 readiness
  • Developer productivity and platform engineering

Technical Leadership

  • Fractional CTO / VP Engineering
  • Engineering team building and mentorship
  • Architecture and technical strategy
  • Startup-to-scale transitions

Education

PhD, Materials Engineering Focus: Computational Physics, Molecular Dynamics, Statistical Mechanics

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