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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.

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Machine Learning Engineers

Skills
  • Ongoing monitoring for accuracy drift
  • Retraining scheduled before performance drops
  • Clear metrics agreed before training starts
  • Metric-based reviews
  • Training pipelines
  • Evaluation results
Engagement models
  • Full-time dedicated
Stack
  • F1 score
  • Precision
  • Recall
  • Retraining
  • classification
  • forecasting

Typical tasks

  • Models trained on your own historical data
  • Clear metrics agreed before training starts
  • Honest reporting on where accuracy falls short
  • Ongoing monitoring for accuracy drift
  • Retraining scheduled before performance drops
  • Alerts if a model starts behaving unexpectedly

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.

  • No-cost replacement

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

Why teams hire machine learning engineers through TCB Infotech

  • 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.

  • 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.

  • Direct access, no relay

    You manage the engineer's day to day work directly, on your tools and your process.

  • Models you can keep

    Training pipelines and evaluation results are documented and handed over cleanly if the engagement ends.

  • No long lock-in

    Scale the engagement up or down with notice, based on your actual modeling roadmap.

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.

  • AI team architect

    Designs the data, model and serving layers before your team builds.

  • Dedicated AI team

    A pre-matched team of four roles: architect, engineer, backend and frontend developer.

  • Dedicated AI engineers

    Vetted AI engineers who take a feature from raw data to a deployed, monitored system.

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

No commitment. See a shortlist before you decide.

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