Senior Data & ML Platform Engineer | dutchengineer.substack.com | Host of Ask A Data Mentor Podcast

Built real-time ML systems delivering millions of daily inferences. Integrated LLMs that saved over 1,000 hours per week across the company. Designed scalable MLOps platforms that took model deployment from months to days.

I have spent the last 8+ years driving AI and ML engineering efforts across startups and high-growth companies. My work spans productionizing models, leading company-wide ML initiatives, and building systems that work. 

Highlights:
Led LLM integration at Equipmentshare, embedding OpenAI into internal tooling to save 1,080+ hours weekly
Deployed Zwift’s real-time racing score model, handling 8.6 million inferences per day
Cut ML deployment times from months to 3 days with standardized CI/CD and packaging
Rolled out Databricks company-wide, reducing deployment cycles by 4x
Built ML pipelines for pricing $3B+ in fleet assets, churn risk, and invoice automation

I write often on machine learning systems and infrastructure on dutchengineer.substack.com, and I enjoy helping teams ship fast, reliable ML in production.

Keywords: Airflow, AWS (Cloudwatch,  ECS, EKS, Fargate, Glue, Redshift, Kinesis, Databricks, Postgresql, S3), Azure (Azure Functions, Azure ML Studio, Databricks), BentoML, C++, Datadog, Docker, ElasticSearch, Flink, Kafka, Keras, Kubernetes, Hive, High Performance Computing, Github Actions, Gitlab CICD,  Pagerduty, Prefect, Python (DBT, Numpy, Pydantic, Sklearn,Scipy), Pyspark, Neural Networks, Natural Language Processing, MLFlow, MongoDB, Recommendations, Sagemaker, Scala, SparkML, SQL, Tableau, Terraform, Pytorch, GPU

Sarah Floris, MS

In my day job, I help data science teams 2-3x their productivity/happiness. Outside, I make content to help others do that, too.

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I'm an ML platform engineer. What is that?

Yes, Platform Engineering is rebranded DevOps. But the rebrand is trying to get back what DevOps was originally meant to be. And I'm trying to do that for MLOps and Data Science!

It's a super challenging problem because:

• SWE's speak a different language: often, the "best practices" for handling data defy the conventional wisdom in "traditional software engineering". Result: interactions between DS and SWE are stressful.

• Simplicity: MLOps--like DevOps--is a practice, not a person. MLOps is best practiced by the people creating inference pipelines. For DevOps, SWE's are the practitioners. But for MLOps, it's... statisticians, academics, analysts, and domain experts. Trying to force them to be Kubernetes, docker, or even git experts won't work.

• Risk: ML/AI is about making inferences (guesses). By definition, when you're guessing, it's impossible to write tests that guarantee if your thing works before deploying. You *have* to test in prod. (they call it A/B testing).

ML platform engineering is a new field. And it's awesome.

Eric Riddoch

Big step, be proud of me: I actually put myself on an grown up sleep/work/exercise schedule instead of staying up until 3 watching Crunchyroll 🤣

Apparently, it’s terrifying what Im capable of without structure.

Now that the holidays are over, my wife is officially my boss (accountability person).

She worked 81 hours this last week (pediatrics resident on night shifts). So… I now that I’ve had 2 weeks to play, it feels extra wrong not to be productive knowing that she’s working so hard for us.

She’s supportive of whatever I do this year: writing courses, job searching, taking a break, etc. but as long as I don’t have a job I need to be able to show that I’m driving towards SOMETHING productive.

===

Did a few more job interviews.

Got HTTPS working for public AND internal (tailscale vpn) services on my k3s homelab… unfortunately it’s still somewhat brittle.

I’ve learned a lot through setting up this homelab. I’ll write a Substack about my setup as a way of asking smarter people how I should have done it better.

Still no progress on the course since October last year. We’ve got a concept 🤣 All 4 of us involved are in a weird state of job searching which makes scope hard to nail down, because we’re only half available and any of us may need to stop once we start a job. We’ll see.

🎧 Eric Riddoch

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Been playing with my kubernetes homelab... and I'm drowning in mental models 🤣

1. SSL/TLS (who terminates, certificate authority, DNS/Email challenge)

2. tunneling, network interfaces

3. has-a/is-a relationships between ingress, service, loadbalancer, controller

4. traefik: routes, middleswares, static/dynamic config

5. when (not) to use namespaces... apparently when you have secrets that need to be accessed between them like a TLS cert

I'm working on a "dual stack" network where private/internal services are accessible via a tailscale VPN, and public/external services are accessible via a cloudflare tunnel.

Internal is done with tailscale and traefik.  Now figuring out external with cloudflare tunnels and traefik.

I'll draw a diagram and ask for feedback when I get a baseline working.

I said yes to some recruiters. They all asked for my availability. I gave them all all my availability. 

Now they are booking overlapping times, and I email them and say, “sorry could you pick again?” And I’m sure they say to themselves “I thought he said he was available at this time.” Repeat 😅

They’ve all got their own system: text in an email, something like calendly, etc.

[edit] Haha, thanks guys. Okay, fine, I'll start pushing them all to use my calendar link. 

I'll say something like "I filled out my availability in your form. Also, here's my calendar link to double check if spots are available, since I shared the same availability with a few groups."

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Sarah Floris, MS

Eric Riddoch

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Eric Riddoch

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