Hire machine learning engineers who take models to production
Hire machine learning engineers from our in-house team in India. They prepare your data, train and evaluate the model, deploy it behind an API or a batch job and monitor it after launch. You interview the shortlist and choose who joins.
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What a machine learning engineer does on your team
Most of the work sits around the model, in the data pipeline before training and in the deployment and monitoring after it.
- What does a machine learning engineer do?
- A machine learning engineer builds software that learns from historical data to make predictions, such as a demand forecast, an equipment failure alert or a package category. The role covers data preparation, feature engineering, model training and evaluation, deployment to production and monitoring for drift, so the model keeps working after launch.
ML engineers and data scientists available to hire
Hire one engineer for a first model, or pair roles when the data or the platform needs its own owner.
Machine learning engineer
Builds forecasting, scoring and classification models on your historical data, from feature engineering to a deployed API.
See machine learning developmentComputer vision engineer
Builds image and video models that inspect parts, count items and read documents or drawings.
Data scientist
Runs exploratory analysis and feasibility checks that tell you whether your data can support a model before you fund the build.
Time-series and IoT ML engineer
Builds models over sensor and event streams, such as failure prediction for manufacturing equipment or delivery time estimates for a fleet.
MLOps engineer
Pipelines, model registry, rollouts and monitoring for teams that run several models in production.
Hire MLOps engineersData engineer
Pipelines and a warehouse that deliver clean training data on time, for when the data is the bottleneck.
See data engineering servicesFour gaps that sink ML hires
Training a model in a notebook is a common skill. Keeping one accurate on live data is rarer, and our vetting looks for these four gaps.
Notebook-only experience
A model that scores well on a clean extract can fail on late features, missing values and live traffic. References and work samples confirm models that ran in production.
Leaky evaluation
Features that carry the outcome make a model look accurate until launch day. The live build exercise is reviewed for how the candidate splits data and builds the hold-out set.
No plan for drift
Prices, customers and equipment change, and an unmonitored model degrades quietly. Our ML engineers ship drift monitoring and a gated retraining pipeline with the model.
Deep learning by default
A neural network on a tabular problem costs more compute and is harder to explain. Engineers start from a baseline, often gradient boosting on tabular data, and add complexity only when it wins on the hold-out set.
How we vet machine learning engineers
Every engineer on the roster is full-time and in-house. You see the vetting notes during CV review, before you commit to anything.
Hire an ML engineer this month
Tell us what you want to predict and where the data lives. You get vetted profiles within 48 hours.
Predictive maintenance for warehouse equipment at a global logistics provider
ScalaCode built a predictive maintenance system for the warehouses of a global logistics provider. IoT sensors track equipment health, a Python machine learning model predicts maintenance needs, and a Vue.js dashboard alerts staff before equipment fails.
equipment monitoring
What our ML engineers build with
Engineers fit the stack to the data platform and cloud you already run.
Cost to hire machine learning engineers
Dedicated ML engineers start from $1,799 a month, full-time. If you want to test the idea first, a proof of concept on your own data starts at $4,999.
| Dedicated team | Fixed-scope project | Team extension | |
|---|---|---|---|
| How it is priced | From $1,799 a month per engineer, full-time | Fixed quote after a free discovery sprint | Billed monthly per engineer |
| Best for | Ongoing model work on a product or platform | A defined build with agreed milestones | A specific skill gap in a team you already run |
| Who manages day to day | You, inside your process | ScalaCode, against agreed milestones | Your leads, with our engineers reporting in |
Monthly means full-time and dedicated, 160 hours. Compare every option on the Engagement models page.
What clients say about our AI engineers
Verified reviews from Clutch and GoodFirms, and clients on camera.
33 verified reviews on Clutch
Clutch review★★★★★“They didn't just build an algorithm; they built a tool that respects the nuances of Indigenous reconciliation.”
Clutch review★★★★★“What truly distinguishes ScalaCode from others is their ability to understand industrial domain logic.”
Clutch review★★★★★“What sets ScalaCode apart is their technical AI expertise and an understanding of a client's organizational ethos.”
GoodFirms review★★★★★“They built a high-performance, AI-powered platform that transformed video engagement for learning.”
Clutch review★★★★★“What stood out to us as most impressive and unique about ScalaCode was their ability to humanize complex AI.”
Clutch review★★★★★“They managed to balance technical skill with an understanding of the marketing world.”
Clutch review★★★★★“Their team often proposed innovative features we hadn't considered.”
Hire machine learning engineers FAQs
Dedicated ML engineers start from $1,799 a month, full-time, the same rate as every other role.
How much does it cost to hire a machine learning engineer?
Dedicated ML engineers start from $1,799 a month, full-time and billed monthly. A proof of concept on your data starts at $4,999, and project work starts with a free discovery sprint.
Should we hire a data scientist or an ML engineer?
Hire a data scientist while the question is still open, such as whether your data can predict the outcome at all. Hire an ML engineer once you know what to predict and need a model running in production with an API, monitoring and retraining. For a first model, one ML engineer usually covers both.
How do you vet machine learning engineers?
Every engineer is a salaried member of our in-house team who passed a systems design interview and a live build exercise. References and work samples confirm production ML work, and you see those notes with each profile.
How fast can an ML engineer start?
Matched profiles reach you within 48 hours of the first call. After you interview and choose, onboarding takes about a week.
Our data is messy. Can an ML engineer still help?
Yes, and most projects start there. The engineer audits the data first and tells you which fields can support a model and which need cleaning or more history. If the data needs pipelines before modelling, a data engineer can join the work.
Will the engineer deploy and monitor the model, or only train it?
They deploy it. Our ML engineers put models behind an API or a batch job, monitor inputs and predictions for drift, and build a retraining pipeline that promotes a new model only after it beats the current one on a hold-out set. Teams running many models add an MLOps engineer.
Who owns the models, code and training data?
You do. We sign a mutual NDA before sharing CVs, and the code, trained models and evaluation sets transfer to you under the engagement terms. Engineers work in your cloud accounts and repositories, so the training data stays where it is.
What happens if the engineer is not a good fit?
Tell us and we share new profiles. You interview the replacement the same way, and a change usually needs one to two weeks' notice.
Which time zones do your ML engineers cover?
Our engineers work from India and overlap with US, UK and European business hours. For US clients the standard overlap is 4 to 6 hours of the US working day.
Your engineers work in your hours
Timezone overlap is built in. Your matched engineers keep 4 to 6 hours of daily overlap with US hours and a full working day with the UK, Europe and the Middle East, so standups, reviews and fixes land during your working day.
What sets ScalaCode apart is their technical AI expertise and an understanding of a client's organizational ethos.
Tell us what you want to predict and we will send matched ML engineers
- Profiles matched on models, stack and domain within 48 hours
- Vetting notes shared with every profile
- You interview and choose who joins
“They delivered on time and even offered ideas I hadn't considered.”
John-Paul Hatala, CEO, SnagPad