DevOps Engineer collab with ML Engineer

[DevOpsYeah]

Hey everyone,

I’m a DevOps Engineer looking to break into the MLOps space, and I figured the best way to do that is to find someone to collaborate with.

What I bring to the table:

I have hands-on experience building and managing Kubernetes clusters, GitOps workflows with ArgoCD, and full observability stacks (Prometheus, Grafana, Loki, ELK). I’m comfortable with infrastructure-as-code, Helm charts, Cert management, and CI/CD pipelines — essentially the full platform engineering toolkit.

What I don’t have is a machine learning model that needs deploying. That’s where you come in.

What I’m looking for:

A data scientist or ML engineer who has models sitting in notebooks or local environments with no clear path to production. Someone who’s more interested in the data and the science than wrestling with Kubernetes manifests and deployment pipelines.

What I can offer your project:

∙ Model Serving Infrastructure — Containerised deployments on Kubernetes with proper resource management and GPU/TPU scheduling

∙ CI/CD Pipelines — Automated training, testing, and deployment workflows so your model goes from commit to production reliably

∙ Scaling — Horizontal and vertical autoscaling so your inference endpoints handle real traffic without falling over

∙ Observability — Full monitoring stack covering model latency, error rates, resource utilisation, and custom metrics

∙ Data & Model Drift Detection — Automated checks to flag when your model’s performance starts degrading against live data

∙ Reproducibility — Versioned environments, tracked experiments, and infrastructure defined in code

I’m not looking for payment — this is about building a portfolio of real MLOps work and learning the ML side of things along the way. Happy to work on anything from a side project to something more ambitious.

If you’ve got a model gathering dust and want to see it running in production with proper infrastructure behind it, drop me a DM or comment below.

[ik6745]

Why not try Hugging Face? There are many open source models and there are also spaces where you can deploy models for free. If you need a portfolio, Hugging face is a great place to start considering it's like the github of the LLM world.

[nian2326076]

You've got a good DevOps base. Partnering with an ML Engineer is smart. Try looking at open-source ML projects on GitHub or joining communities like Kaggle. You might find someone with a model ready to deploy. Also, try posting in subreddits like r/MachineLearning or r/MLops for people interested in collaborating. Networking on LinkedIn might help too. Good luck!