Parvessh Agarwal
Founder & CEO
An entrepreneur at heart. He links business analysis, strategy, clear goals and ROI to practical technology.
LinkedIn →Hire machine learning engineers
who take models from a notebook to production.
A machine learning engineer builds models that learn from your own data. Examples are predicting which customers will leave, sorting support tickets or forecasting demand. PixelCrayons scopes the role and writes the proposal. We then find and supply the engineer, or a small team. You interview the actual builder before anything is signed. Want a defined AI build as a project instead? See our custom AI development service.
Interview before signing · One contact throughout
Tell us about the role
Start Your Enquiry
Send a short brief. Once we have a complete brief, we confirm the date you will receive your itemised proposal.
What you get
Hire a machine learning engineer through PixelCrayons, with one point of contact. We find an engineer who has trained models on real business data and run them in production. They test each model against data it has not seen. You interview them before signing. One account team handles escalation, billing and status.
- Vetted on models in productionEvery engineer we propose has run a model in production, vetted to our delivery standards.
- One point of contact for everythingPixelCrayons scopes the role and handles escalation, billing and status. Our engineer builds the model.
- Honest advice on the right routeYou hear early if a simpler rule, an AI developer or a scoped project fits better.
Our work
See what we've built
and the results it brought.
Real projects for real clients, with results we can show.
2004
Established
500+
Agencies served
4,500+
Projects delivered
340%
Revenue growth · 7 months
eCommerce
+127%
Organic traffic · 5 months
SaaS
85%
Faster delivery · backlog cleared
via agency partner
Where the work happens
What they cover
Machine learning engineer skills
for models that work on live data.
Each engineer is vetted on models that ran in production, not only in a notebook. Each one is matched to your gap.
Building the model
- Preparing your data: finding gaps, fixing labels and splitting it fairly for testing
- Prediction and classification models, such as which leads will convert or which tickets are urgent
- Forecasting demand, stock or revenue from your own history
- Recommendations built on what your customers actually buy or read
- Choosing a simple model first, and a complex one only when it earns its place
Running it in production
- Packaging the model so your app can call it reliably
- Pipelines that retrain the model on fresh data on a schedule
- Monitoring for drift, when live data stops looking like the training data
- Version control for data and models, so a bad release can be rolled back
- Cost and speed tuned to real traffic, not a test run
Working well with your team
- Working with your data owners on what may be used, and what may not
- Explaining results in plain terms your team can act on
- Pointing you to a simpler rule or report when a model is not needed
- Notes that let another engineer retrain and maintain the model after handover
- Clear reports on how the model performs on live data, not only in testing
In practice
What a machine learning engineer
does after the first model.
Practical notes for briefing, interviewing and working with the person who builds your models.
Most of the work is on the data
Training a model takes a small part of the week. Most of the time goes on the data around it. The engineer finds missing values, fixes wrong labels and checks that test data is kept apart. They build the pipeline that feeds new data in. They also watch how the model does on live data. Expect more changes to data code than to the model itself.
Interview questions that show real experience
Ask the candidate about a model they put into production, and how it did six months later. Strong candidates talk about drift, data that changed, or a feature that leaked the answer into training. Ask how they knew the model was good enough to ship. A good answer compares it with a simple baseline. A weak answer quotes one accuracy number with no comparison.
Have your data and your question ready
Before the first day, write down the decision the model should help with. Gather the data you have, with a note on where it comes from and how far back it goes. Agree who owns the data and what may be used. Decide how you will judge success, in business terms. Settle these early and the first week goes on building, not waiting.
Why machine learning hires go wrong
The common failure is starting with a model before there is a clear question. The second is too little data, or data that does not match what the model will see live. The third is a model that works in testing but has no plan for running, retraining or monitoring. Write down the question, the data and who looks after the model once it ships.
When this is the wrong hire
If you want to add a chatbot or a language model feature to your product, an AI developer fits better. If your data lives in many places and nobody trusts it, an analytics engineer should clean it up first. If you need a large language model trained from scratch, that is research work with different skills. This hire works best when the question and the data are already clear.
How it works
How hiring works,
with one contact throughout.
Start
Brief and a call
We sign an NDA, and you talk PixelCrayons through the question, the data and the gap.
Once the brief is complete
Proposal and interview
We confirm when your proposal will arrive. We name the engineer whose track record fits. You interview them directly before anything is signed.
Your decision
You sign off
You confirm the scope, the payment and the access we need. Nothing starts before this.
Typically 2 to 3 business days later
The engineer starts
Once they have data and code access, they review your data and join your stand-ups.
Every month
Scale up or down
Add a specialist when the work needs one, or step down once the model ships. The same PixelCrayons account team handles it throughout.
You make requests, give approvals and see the weekly review in your Prism workspace. Each decision is written down with the result it was meant to bring. See how Prism runs the work →
Get a Proposal
Meet the people who will do the work before you sign
Why through us
Why hire through us:
one contact and the right engineer.
PixelCrayons defines the role, supplies the engineer and manages your account. You deal with one contact and get the right builder. It is always clear who answers for the model.
Vetted on models in production
Every engineer we propose has run a model in production before yours. They are vetted against our own delivery standards. A notebook shows the model on old data. A track record shows what it does when live data changes.
One point of contact for everything
PixelCrayons scopes the machine learning role and writes the proposal. One PixelCrayons team then handles escalation, billing and status. Our engineer builds the model.
Honest advice on the right route
You hear early if a simple rule or a report would do the job. You also hear if the work suits an AI developer or a scoped project for our custom AI development team. We never stretch a hire to cover work they can't do.
One company answers for the work
You sign with PixelCrayons, and we answer for every commitment in the proposal. That holds for as long as the work runs. It stays clear who answers for the model long after it goes live.
Side by side
Hiring on your own,
compared with hiring through us.
An in-house hire is worth it once models become core, ongoing product work. We'll tell you plainly if that is where you are. Most machine learning work starts smaller, or fits a scoped project better than a hire.
| Hiring it yourself | Through PixelCrayons | |
|---|---|---|
| Time to a working model | A full recruiting cycle of sourcing, interviews and notice periods, then a model that never leaves the notebook | Eligible work can typically start two to three business days after scope, payment and required access are confirmed |
| Vetting | A strong interview on theory. Problems with live data show up after launch | A track record on models in production, checked against our own standards |
| Management work | All yours: recruiting, then checking how each model holds up on live data | PixelCrayons' account team handles the day-to-day contact; you review the results and set priorities |
| Number of suppliers | However many agencies or freelancers it takes to cover the gap | One proposal and one point of contact, whichever engineer is assigned |
| Risk when it doesn't work | A model gets quietly worse as data changes, with no monitoring to catch it | If the fit is wrong, we propose someone else; leaving takes a handover call with notes, not a negotiation |
Who is accountable
The people responsible
for your work.
Saundaraya Gupta
Technical Manager
Brings technical work, client updates and project management together, so work moves smoothly from brief to launch.
LinkedIn →Every engagement
A named coordinator and escalation contact
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Before you sign
Our commitments
Each one says where it applies and links to its full terms.
Time-zone overlap
At least four hours a day inside your core working hours. Two hours plus a daily written handover for the US West Coast.
Managed specialists only; not a staffing promise for project teams.
Full termsClear IP & Ownership Terms
Your materials remain yours. Bespoke deliverable rights, licences and handover are agreed before work starts.
All engagements.
Full termsClear Delivery & Escalation Ownership
Know who coordinates your engagement, who owns the work and how to escalate an issue.
All engagements. Roles may be combined and vary with your engagement.
Full termsWhy not just hire a freelancer?
A freelancer can be the right choice for a single, well-defined task. We are set up for work that needs an accountable team: a named coordinator, delivery reviewed by QA and a senior lead before release, and continuity arrangements written into the engagement. Confidentiality, ownership and escalation terms are agreed in writing before work starts.
How does time-zone overlap work?
Your engineers work at least four hours a day inside your core working hours. We set the window with you before the engagement starts and hold it for as long as the engagement runs. For the US West Coast the live overlap is two hours each morning, with a written handover at the end of every Indian day. We do not run night shifts: engineers who work nights leave quickly, and keeping the same people on your team matters more to us than a longer overlap.
Can we try you before we commit?
For eligible managed specialists, yes: up to 14 calendar days or 80 logged working hours, whichever comes first. Stop within it and eligible trial hours are free; continue and they're billed at the agreed rate. Unpaid trial work stays ours. Project work and other roles get a paid pilot with scope, acceptance criteria, duration and fee agreed first.
What if we want to stop?
Cancellation, notice and any renewal terms are written into your proposal or statement of work before you accept it. If an engagement ends early, we hand over completed, paid-for work and list any unfinished work separately. For an eligible specialist trial, you can stop within the trial window and pay nothing for eligible trial hours.
How we handle claims, credentials and your data: group credentials, dated ratings, how case figures are signed off, client permissions and data handling.
How we vet
How we vet a machine learning engineer
and what to ask them.
We propose engineers vetted on models in production. Your interview is the second check. These five questions show whether a candidate ships models or only trains them.
| Ask about | A strong answer | A weak answer |
|---|---|---|
| A model they put into production, and how it did six months later | Drift, data that changed, or a feature that leaked the answer, and what they did about it. | The launch, and nothing after it. |
| How they knew the model was good enough to ship | A comparison with a simple baseline, on data the model had never seen. | One accuracy number with no comparison. |
| What they need from you before the first day | The decision the model supports, the data and where it comes from, and how success is judged. | Access to a GPU, and nothing about your data. |
| How they would spot the model getting worse | Monitoring that compares live data and results with what the model was trained on. | Users would complain. |
| When a model is the wrong answer | A simple rule or report when it does the job, or cleaning the data first. | Never. Every problem gets a model. |
A chatbot or language model feature in your product usually suits an AI developer. A defined AI build with an end date often fits our custom AI development service better than a hire.
Questions
Frequently
asked.
The engineer you interview and work with comes from our Vinove group engineering team. PixelCrayons scopes the role, writes the one proposal and stays your contact throughout. You contract with PixelCrayons, and we answer for every commitment on this page.
A machine learning engineer trains models on your own data, such as forecasts or classifiers. An AI developer mostly connects existing language models, like the ones behind ChatGPT, to your product. Many projects need one or the other, not both. We'll tell you on the first call which fits.
It depends on the question. Some models work well on a modest amount of clean data; others need far more. The engineer checks your data in the first days and tells you plainly if it is enough, or what to collect first.
One. You sign with PixelCrayons, and one account team handles the whole relationship. Escalation, billing and status updates all come through the PixelCrayons account team you signed with.
Tell us early, and we propose a replacement. The new engineer gets the data notes, model versions and test results the first one kept. So a switch costs days, not a restart.
Get one proposal
with the right engineer for your data.
Tell PixelCrayons about the question and the data you have. We scope the role, supply the engineer under a written proposal and stay your point of contact throughout. You interview the actual person before anything is signed. If a scoped AI project fits better than a hire, we'll say so on the first call.
Interview before signing · One account team
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