Forward deployed engineers for MLOps and model deployment
Use cases / MLOps
Your data scientists have a model that works in a notebook. Forward deployed engineers for MLOps join your team, part of the week or for a fixed period, and get it serving real traffic, monitored and ready to retrain. You tell us who you need. We send people who fit.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Why models stay in notebooks
Training is a project serving is a system
Many machine learning teams are strong at building models and short on the engineering that keeps them running. That means packaging, serving, consistent features, monitoring and safe rollback.
A forward deployed engineer works beside your data scientists to close that gap. They build the deployment path once, properly, so the next model goes out much faster.
Tell us the role and the hours. We send you a shortlist of people who fit.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
What the work covers
What forward deployed engineers for MLOps build
- Packaging and serving Models served behind an API or batch job, with versioning, autoscaling and clear speed targets, on cloud or your own hardware.
- Feature consistency The same feature logic in training and serving, so the model sees in production what it saw in training.
- Monitoring and drift Tracking input drift, prediction quality and business results, with alerts when a model needs attention.
- Retraining and rollback A repeatable pipeline to retrain, evaluate and promote models, and a fast way back if a new one misbehaves.
How hiring an engineer for your models works
- 01
Send your requirement
Tell us the model, the system that will call it, your speed and cost targets, and the data science lead who signs it off. It is free, and there is no deposit.
- 02
Get a shortlist
We read every requirement ourselves and look for people who fit. Then we send a shortlist: each person's CV and a short note on why they fit.
- 03
Talk to them and choose
You talk to whoever you like and choose. Ask what infrastructure they have run models on: GPUs, on premises or cloud. You do not have to hire anyone.
- 04
Pay only when someone starts
One agreement and one invoice with us, on terms agreed in writing before anything starts. Talent never pays.
Constraints to plan around
| Constraint | What it means for the build |
|---|---|
| Model risk and documentation | In regulated industries, models need documentation, validation and monitoring before production use. The deployment pipeline produces that evidence. |
| Data residency | Training data, features and models may have to stay in a region or on premises. Infrastructure choices follow. |
| GPU cost | Serving cost is tracked from the first deploy, with batching, smaller models or quantisation where quality allows. |
| Reproducibility | Every production model traces back to its code, data and settings, so results can be recreated. |
A platform vendor or a forward deployed engineer
MLOps platform on its own
- Useful building blocks for serving and tracking
- Still needs someone to wire it to your systems
- Can add cost and complexity before it adds value
- Vendor support, not an engineer in your team
Forward deployed engineer
- Uses your existing cloud and tools where possible
- Builds only what this model needs, then generalises
- Pairs with your data scientists every week
- Leaves a path your team can repeat
For companies
Tell us who you need.
Send your requirement. It is free and there is no deposit. We read it ourselves and send you a shortlist of people who fit. You pay only when someone starts.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Fees, how it works and related use cases
Tell us what you need.
Send your requirement. We send you a shortlist of senior leaders who fit. One agreement and one invoice, with the terms agreed in writing before anything starts.
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Send your requirement
- No deposit to get started
- Every requirement read by us
- A best-fit shortlist, not a pile of CVs
- One agreement, one invoice
Apply to join
Talent never pays to apply, be shortlisted or be placed.
FAQ
Questions people ask
Does this cover LLMs as well as classic ML?
Yes. Serving open-weight LLMs on your own infrastructure is a common request, alongside classic models like forecasting and classification.
Which clouds are covered?
AWS, Google Cloud, Azure or on premises. Name yours in your requirement, and we look for people who have worked on it.
Our data scientists already deploy their own models. Is this still useful?
Often, yes. The engineer can standardise what each person does differently, add monitoring and free your scientists to focus on models.
What happens after handover?
Your team runs it with the runbooks, dashboards and pipelines the engineer leaves behind.
Explore more
- How it works
What happens after you send your requirement, step by step.
- Fees
No deposit. One agreement and one invoice when someone starts.
- What is fractional
Senior leaders for part of the week, and when that makes sense.
- Locations
Talent based across Asia, Europe, the Middle East, Africa and the Americas.
How it works
Fees
What is fractional
Locations