STAFF AUGMENTATION

Hire Machine Learning Engineers

Train, validate and deploy models against your metrics, not a leaderboard.

2 weeks

notice period, no minimum term

What a ML engineer does here

A machine learning engineer builds models that earn their place in a system: trained on your data, validated against a metric the business recognises, and deployed somewhere they can be monitored and rolled back.

Most of the value is in the parts that are not training. Data quality, leakage, class imbalance, drift detection and the honest question of whether a model beats the rule that is already in place — those decide whether the project pays for itself.

What they are good at

Supervised and unsupervised modelling on tabular, image and text data
Feature engineering and leakage detection
Cross-validation design that matches how the model will actually be used
Class imbalance, calibration and threshold selection
Model serving, versioning and rollback
Drift monitoring and scheduled retraining
Experiment tracking and reproducibility
Honest baselines — including the rule-based one

What they build

Prediction services

Scored endpoints with versioned models, calibrated thresholds and a documented retraining trigger.

Segmentation and vision models

U-Net and transformer architectures where the output feeds a human decision rather than replacing it.

Ranking and scoring

Estimation and prioritisation models measured against the business outcome, not offline accuracy alone.

The stack

PythonPyTorchscikit-learnXGBoostMLflowAirflowDockerAWS SageMaker / GCP Vertex

Proof

NHS-grade clinical AI cutting reporting time by 85%

Deep-learning CT analysis with role-based clinical workflows and automated reporting for lung nodule risk stratification.

>0.85 Dice coefficient92% classification accuracy85% faster reporting

How you can engage them

Dedicated

Full-time on your product, in your standups and your repository. The right shape when the work is continuous and context compounds.

Extended team

Part-time capacity alongside your own engineers, for a specific workstream or a gap you are hiring against.

Project

A defined scope with an agreed outcome and end date. Best where the requirement is clear and unlikely to move.

We do not publish rates, because the honest number depends on seniority, duration and notice. Tell us the budget you are working with and we will tell you what it buys — or say plainly if it does not buy enough.

How it actually starts

Scoping call

48 hours

What you are building, which skills it needs, and whether we are the right answer. Sometimes we are not, and you will hear that.

Profiles

3–5 days

CVs and code samples of the specific people available, not a generic capability deck.

You interview

your process

Technical interview with the actual engineer. Reject anyone you are unsure about — that costs nothing.

Trial period

first 2 weeks

Real work in your repository. If it is wrong, you stop, and there is nothing further to pay.

Onboarded

week 3

Shipping in your process, with a written record of decisions from the first day rather than the last.

Working across time zones

We are in Bangalore. That is an advantage for some of your working day and a constraint for the rest, and we would rather state the number than imply there is no gap:

London

5 hours of overlapping working day

Dubai

8 hours of overlapping working day

Singapore

7 hours of overlapping working day

New York

No natural overlap — async handover before your morning

San Francisco

No natural overlap — async handover before your morning

Sydney

5 hours of overlapping working day

Contract, ownership and exit

You own everything

IP assigns to you from the first commit. Work happens in your repository under your licence.

Two weeks notice

No minimum term past the first month, no exit fee, no penalty clause.

No lock-in by design

Standard tooling, documented decisions, no proprietary layer that makes leaving expensive.

Confidentiality

NDA before the scoping call if you want one, and we will sign yours rather than insisting on ours.

Frequently asked questions

How much data do we need?

Less than most vendors imply for a narrow, well-defined problem, and more than anyone wants to hear for a broad one. The first week is usually spent finding out which of the two you have — and sometimes the answer is that a rule beats a model.

Can you work with our data scientists?

Frequently that is the engagement: a team has models that work in notebooks and no path to production. Our engineers take that path and leave the pipeline behind, documented.

Who owns the code they write?

You do, from the first commit. Work is done in your repository under your licence, and the contract assigns IP to you outright. We keep no rights over what we build for you and no dependency that makes leaving expensive.

What if it is not working out?

Two weeks written notice, no penalty, no minimum term beyond the first month. An engagement that needs a contract to hold it together has already failed — we would rather you could leave easily and chose not to.