Paul Iusztin

🤖 Senior Machine Learning Engineer • MLOps • Contractor ~ Helping startups engineer production-ready ML/AI systems.

I'm the author of the bestseller LLM Engineer's Handbook, lead instructor of the Agentic AI Engineering course, founding AI Engineer of a San Francisco start-up, and obsessed with making knowledge accessible through AI.

With over 10 years of experience in AI and software and 20 apps shipped, I teach AI Engineering as I wanted to at the beginning of my career. End-to-end. From idea to production. From data collection to deploying, monitoring and evaluation. With a focus on AI principles, software patterns and infrastructure systems that will thrive in a future dominated by AI coding tools.

My ultimate goal is to help other engineers escape PoC purgatory and 10x their AI Engineering skills.

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If you want to become an AI Engineer pro, join Decoding AI for exclusive content on designing, building, and shipping AI software that works: 

🗞️ https://www.decodingai.com

See more:

🌐 Homepage: https://www.pauliusztin.ai
📚 LLM Engineer's Handbook: https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/
🤖 Agentic AI Engineering Course: https://academy.towardsai.net/courses/agent-engineering?ref=b3ab31

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✉️ For collabs, contact me at: p.b.iusztin@gmail.com

Senior Machine Learning Engineer

🟡 Scalable ML batch infrastructure for continuous training & inference

The client, a generative AI leader, struggled with manual training and calling their ML models. As a tedious and unscalable process, they needed to automate their ML processes completely and integrate them with their current web platform.

➞ 𝐃𝐞𝐬𝐢𝐠𝐧𝐞𝐝 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a modular 𝐏𝐲𝐭𝐡𝐨𝐧 𝐩𝐚𝐜𝐤𝐚𝐠𝐞 that composes the ML model required steps into 3 main batch pipelines that can easily be automated: feature, training, and inference, 𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐭𝐢𝐧𝐠 a 𝐭𝐞𝐝𝐢𝐨𝐮𝐬 𝟐-𝐡𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬/𝐯𝐢𝐝𝐞𝐨.
➞ 𝐃𝐞𝐩𝐥𝐨𝐲𝐞𝐝 a scalable & cost-effective 𝐛𝐚𝐭𝐜𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 and 𝐂𝐈/𝐂𝐃 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 on top of AWS ECS & SQS that scales in and out based on the number of jobs, 𝐬𝐚𝐯𝐢𝐧𝐠 𝐭𝐡𝐨𝐮𝐬𝐚𝐧𝐝𝐬 𝐨𝐟 𝐝𝐨𝐥𝐥𝐚𝐫𝐬/𝐦𝐨𝐧𝐭𝐡.
➞ 𝐒𝐚𝐯𝐞𝐝 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐟 𝐟𝐮𝐭𝐮𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 by using good software practices to make the Python module & batch cloud infrastructure flexible and composable.
➞ 𝐁𝐮𝐢𝐥𝐭 a 𝐯𝐨𝐢𝐜𝐞 𝐬𝐲𝐧𝐭𝐡𝐞𝐬𝐢𝐬 𝐏𝐨𝐂 in ~1 week required to 𝐜𝐥𝐨𝐬𝐞 the 𝐝𝐞𝐚𝐥 with one of the 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐜𝐥𝐢𝐞𝐧𝐭𝐬.

⚫️ Big data ingestion & archiving pipeline

The client wanted to expand its platform features by securely and safely ingesting, preprocessing, and storing TB of data into their platform.

➞ 𝐂𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 to the 𝐝𝐞𝐬𝐢𝐠𝐧 and 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 of a 𝐝𝐚𝐭𝐚 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐨𝐧 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports 𝐢𝐧𝐠𝐞𝐬𝐭𝐢𝐧𝐠 >𝟓 𝐓𝐁 of data in 𝐨𝐧𝐞 𝐠𝐨 in a reliable, fast, and secure way.
➞ 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐞𝐝 a scalable 𝐝𝐚𝐭𝐚 𝐩𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞 that supports multiple media types (images, videos, audio, 3D objects) and creates small preview files that are cheap to transfer, store and visualize. The 𝐨𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐟𝐢𝐥𝐞𝐬 are 𝐚𝐫𝐜𝐡𝐢𝐯𝐞𝐝 on an affordable storage solution 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐜𝐨𝐬𝐭𝐬 𝐛𝐲 𝐮𝐩 𝐭𝐨 𝟖𝟏%.

I built a system that turns a single idea into a publish-ready technical article.

Here's a mini masterclass on how (so you can copy):

The system consists of 2 parts:
 
• Nova (research agent): exploratory, steerable
• Brown (writing workflow): deterministic, auditable

𝟭) 𝗡𝗼𝘃𝗮: 𝗦𝘁𝗲𝗲𝗿𝗮𝗯𝗹𝗲 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 (𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻)

Goal: ingest context from messy sources, fill gaps, and produce a clean research artifact.

Inputs:
 
• Structured article brief (topic, audience, structure, constraints, candidate sources)

How it runs:
 
• MCP-first design so tools are portable across harness clients
• A prompt exposed as procedural memory to plan and glue all stages
• Tooling focused on ingesting context, not “thinking in tokens”

Core stages:
 
• Pull context from URLs, local files, GitHub repos, YouTube transcripts
• Run multi-round research loops (Perplexity for cited web search)
• Filter for trust and relevance
• Deep scrape the top sources
• Consolidate into a single handoff artifact: research.md

Output:
 
• A curated research document with organized notes and citations, ready for drafting

𝟮) 𝗕𝗿𝗼𝘄𝗻: 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻)

Goal: turn research into a consistent article, with quality gates and checkpoints.

Why a workflow, not an agent?

Because writing quality demands:
 
• Repeatability
• Checkpoints
• Clear stage boundaries
• Traceability when something is off

How it runs:
 
• LangGraph for stateful orchestration, resumability, and auditability
• Leveraging tools as deterministic workers instead of exposing them to agents

Core stages:
 
• Load context: guideline + research.md + writing profiles
• Generate required assets in parallel (diagrams, images, tables when needed)
• Draft the article
• Run review and rewrite loops (reflection scores, style judge, revision triggers)
• Support human edits without breaking the system’s rules

Outputs:
 
• Final article
• Generated assets + metadata
• Reflection scores and traces (so you debug quality instead of guessing)

Here’s the gist…

If you promise “production-grade writing”, you need exploration for research and durability for execution.

This design choice is the difference between a cool demo and a system you can ship.

Want to learn how to build it out yourself?

We break this down step-by-step in the Agentic AI Engineering course (built with Towards AI, Inc.):

Enroll here: https://lnkd.in/dtxZuZpb

P.S. We’ve opened up another 100 seats for $499. The last batch sold out in under 14 days → get in early to lock in our best offer.

Senior AI Engineer • Founder @ Decoding AI • Author @ LLM Engineer’s Handbook ~ I ship AI products and teach you about the process.

5X your AI & Data Science skills. Get free AI guides and resources ⬇️

I didn’t start in tech. I studied English Literature and thought I’d build a life around writing. But somewhere along the way, I got curious about how digital products are built. That curiosity slowly pulled me into the world of product building.

I began as a Product Analyst. I used every chance I got to ask questions, break things, fix them, and learn. Over time, I moved into an Associate Product Manager role. It wasn’t easy, but every small step added up.

These days, I explore AI tools almost every day. I’m fascinated by how they’re changing the way we work. I test AI agents, AI automations, and AI models just to see what’s possible. I also share what I learn on LinkedIn to help others in the same space.

Leon Jose

Engineers are shipping at 5X speed with Claude Code,

Product Managers are slowing down.

Aakash Gupta shared a playbook for how PMs can actually leverage Claude Code instead of debating it.

Some PMs are still asking what AI is really useful for.

Ironically, that hesitation puts them directly on the hard path.

Others? They’re leaning in.

Using Claude Code to move just as fast as their engineering counterparts.

A clean PM workflow with Claude Code looks like this:

Analyze → Strategize → Build → Multiply

Call transcript → PRD → Tickets → Team update → Metrics view.

All powered by a single system.

Even this basic setup removes friction across teams.

The more advanced, agent-driven setup goes deeper - feeding the model full context: goals, constraints, architecture, existing codebase.

When you optimize for LLM consumption instead of human readability,
Velocity and output quality compound fast.

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Repost this cheat sheet to help your network 💚 

(👉 Follow gotspeed to grow your AI skills)
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Stanford University released 9 LLM courses.

(100% FREE. No Payment required.)

Credit to Luís Rodrigues for the image. 

If you're trying to separate AI hype from architecture decisions, start here:

Lecture 7 (Agentic LLMs) and Lecture 8 (LLM Evaluation) are where most builders need depth.

Full list:
Lecture 1: Transformers
https://lnkd.in/e-5fNJUe

Lecture 2: Transformer-Based Models
https://lnkd.in/eqWSpGNE

Lecture 3: LLMs
https://lnkd.in/e9rMFQm6

Lecture 4: Training
https://lnkd.in/edijGDwg

Lecture 5: Tuning
https://lnkd.in/ennQf9VR

Lecture 6: Reasoning
https://lnkd.in/e_K6-KNS

Lecture 7: Agentic LLMs
https://lnkd.in/e9NnyMxn

Lecture 8: LLM Evaluation
https://lnkd.in/ezFNrUfy

Lecture 9: Recap & Trends
https://lnkd.in/eAadD8XS

Which lecture would change how your team builds?

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♻️ Repost to help your network
➕ Follow gotspeed to grow your AI skills
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