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TCB Infotech | Expert Odoo & ERPNext Implementation Partner

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Machine Learning Engineers

Hire Dedicated Machine Learning Engineers

Add a vetted machine learning engineer to build, train and evaluate prediction, classification and forecasting models against your own data.

50+ ERP and software projects. Zero failed projects since 2021.

Book a Free Consultation → Features
Model Metrics Illustrative
94%
Accuracy
120ms
Latency
0.91
F1 score
Low
Drift this month
1-2 wk
Typical shortlist time
100%
Screened on real model work
Flexible
Full time, part time or project
Free
Replacement if not a fit
Why It Matters

A model that looks good in testing can still fail in production

Real data is messier than a clean benchmark dataset, and accuracy drifts as your business changes. Someone has to own that ongoing work.

Built On Your Real Data

Not a demo model, one trained on your business

A model trained on a generic dataset rarely performs well on your actual sales, inventory or support data. A dedicated machine learning engineer trains and tunes the model specifically against what your business produces.

  • Models trained on your own historical data.
  • Clear metrics agreed before training starts.
  • Honest reporting on where accuracy falls short.
An engineer reviewing model training results
Watched After Launch

Accuracy drifts, so someone has to keep watching

Customer behavior, seasons and product mixes change, and a model that was accurate at launch can quietly get worse. A dedicated engineer monitors this and retrains before it becomes a business problem.

  • Ongoing monitoring for accuracy drift.
  • Retraining scheduled before performance drops.
  • Alerts if a model starts behaving unexpectedly.
Monitoring dashboards for a production model
How Results Are Reported

Metrics you can hold the model to

Every model is scored against agreed metrics before it goes live, and again on a regular schedule after.

Example: A Churn Prediction Model Illustrative
89%
Precision
82%
Recall
Weekly
Retraining cycle
Stable
Drift status
How Hiring Works

From requirements to a working engineer in about two weeks

1
Share requirements
Tell us the prediction or forecasting problem you want solved and what data you have.
2
Shortlist
We match engineers by modeling experience relevant to your problem and send you 2 to 3 profiles.
3
Interview
You interview the shortlist directly and pick the engineer who fits your data and team.
4
Onboard
The engineer reviews your data, agrees success metrics, and starts training in the first week.
5
Scale as needed
Add a second engineer or move to part time once the model is stable in production.
What's Included

Everything you need to run the engagement smoothly

🎯

Dedicated to your project

The engineer works only on your account, not split across other clients' models.

💬

Direct communication

You talk to the engineer directly on your tools, with no account manager relay in between.

📅

Flexible contracts

Full time, part time, or a fixed scope of work. Scale up or down with notice, not a long lock-in.

✅

Metric-based reviews

Model performance reviewed against the metrics agreed before training started.

🌐

Timezone overlap

Working hours arranged to overlap with your team for stand-ups and reviews.

🔐

Replacement guarantee

If the engineer is not the right fit, we replace them at no extra cost during the engagement.

What Changes

What a dedicated machine learning engineer changes for your models

Accurate
Predictions on your real data
Tracked
Performance against agreed metrics
Caught
Drift before it hurts the business
Retrained
On a regular, planned schedule
Honest
Reporting on model limits
Lower
Cost than a full-time direct hire
Why Us

Why teams hire machine learning engineers through TCB Infotech

1
We build models on real business data
We train models against ERP and operational data, so our engineers are tested on messy real data, not clean benchmarks.
2
Fast, honest shortlisting
You see 2 to 3 profiles, not a stack of resumes, and we tell you plainly if a fit is not right.
3
Direct access, no relay
You manage the engineer's day to day work directly, on your tools and your process.
4
Models you can keep
Training pipelines and evaluation results are documented and handed over cleanly if the engagement ends.
5
No long lock-in
Scale the engagement up or down with notice, based on your actual modeling roadmap.
6
Proven delivery
50+ ERP and software projects, with zero failed projects since 2021.
FAQ

Hiring machine learning engineers, answered

What does a dedicated machine learning engineer do?
They build, train and evaluate predictive models such as classification, regression and forecasting, using your own data, and keep those models accurate as your data changes over time.
How is this different from an AI engineer?
A machine learning engineer specializes in training and evaluating predictive models. An AI engineer is a broader generalist who also covers generative features, integration and deployment. If you need someone across the whole build, see our AI Engineers.
Do you work with our existing data as it is?
Yes. The engineer starts by assessing your existing data, flags what needs cleaning, and builds the model around what is actually available rather than an ideal dataset.
How do you know if the model is actually working?
Every model is evaluated against clear metrics agreed with you before training starts, and results are reported honestly, including where accuracy falls short.
What is the engagement model?
You can hire full time, part time, or for a fixed project. Contracts scale up or down as your modeling roadmap changes, with no long lock-in period.
What if the engineer is not the right fit?
We replace the engineer at no extra cost during the engagement, and hand over data pipelines and model work to the new engineer.

Hire a Dedicated Machine Learning Engineer

Book a free consultation. Tell us what you need predicted or classified, and we will shortlist engineers who fit your data and your team.

Book a Free Consultation →

No commitment. See a shortlist before you decide.