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Machine learning development services

Machine learning development services

Senior nearshore ML engineers who turn your data into predictive models running in production, in your time zone.

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Microsoft
Panasonic
Toyota
Applebee's
KPMG
  • 15+
    years delivering software
  • 100+
    senior engineers across LATAM
  • 6-8h
    daily overlap with US teams
  • 6+
    Fortune 500 & global brands trust us
Who We Are

Senior ML engineers who ship models to production

FusionHit's nearshore engineers turn your raw data into deployed, monitored models that drive real decisions. As a machine learning development company, we own the full path from data to production, the part where most projects stall.

WHAT WE BUILD

Machine learning solutions we build

The ML use cases that turn your data into decisions, plus the strategy to get there.

Predictive analytics

Forecast demand, churn, and risk from your historical data, so decisions stop being guesswork.

Natural language processing

Classification, sentiment, search, and summarization that turn text and documents into structured signals.

Computer vision

Image and video models for quality control, detection, and automated inspection.

Anomaly and fraud detection

Spot outliers and fraudulent activity in real time, before they cost you.

MLOps and deployment

Pipelines, monitoring, and retraining that keep models accurate in production as your data changes.

Machine learning consulting

Feasibility, use-case validation, and model strategy when you need direction before you build.

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END TO END

What we own, from raw data to production

The work between a promising idea and a model your business can actually rely on.

Data engineering and pipelines

We collect, clean, and structure your data so the model has something reliable to learn from.

Feature engineering

The signals that decide whether a model performs, built from your domain and your data.

Model training and evaluation

Trained and tested against metrics you agree on, not accuracy claimed after the fact.

Model deployment and serving

Real-time APIs or batch scoring, deployed into your cloud and your product.

Monitoring and drift detection

Performance tracked in production, with alerts when your data starts to shift.

Retraining pipelines

Automated retraining so accuracy holds as behaviour and data change.

Experiment tracking and versioning

Every dataset, run, and model version reproducible, so results can be audited.

Security and governance

PII handling, access control, and model documentation for regulated environments.

TECHNOLOGY EXPERTISE

The machine learning technologies we build with

The frameworks and platforms behind production-grade models.

Frameworks

Training and modeling, from classic ML to deep learning.

  • • TensorFlow
  • • PyTorch
  • • scikit-learn
  • • XGBoost

NLP and LLMs

Turning text and documents into structured, usable signals.

  • • Hugging Face
  • • spaCy
  • • LangChain

MLOps

Tracking, deploying, and retraining models in production.

  • • MLflow
  • • Kubeflow
  • • AWS SageMaker
  • • Google Vertex AI

Data

The pipelines and stores the models learn from.

  • • Snowflake
  • • Apache Spark
  • • pandas
  • • PostgreSQL
Why FusionHit

Why US companies choose FusionHit for machine learning

  • Senior ML engineering talent

    Vetted nearshore engineers who have shipped machine learning to production, across industries and modern stacks.

  • Real-time collaboration

    Your ML engineers work U.S. hours, so model reviews, data questions, and demos happen live, not the next morning.

  • Flexible engagement

    Scale your ML team up or down as the roadmap and priorities shift.

  • Integrated partnership

    Your ML team plugs into your engineers, data, and tools as one unit, not a walled-off external vendor.

FusionHit nearshore machine learning engineers working with a US client team
CASE STUDIES

AI and machine learning we've shipped

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Turn your data into a working model.

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INDUSTRIES

Machine learning across industries

The same modeling rigor, tuned to the data and decisions of your sector.

Healthcare

Diagnostic support, patient risk scoring, and operational forecasting.

Fintech

Fraud detection, credit scoring, and transaction intelligence.

Retail

Demand forecasting, recommendation, and dynamic pricing.

Logistics

Route optimization, ETA prediction, and warehouse automation.

Manufacturing

Predictive maintenance, quality inspection, and yield optimization.

SaaS

Churn prediction, usage analytics, and in-product intelligence.

Explore all industries
HOW WE ENGAGE

Engage an ML team the way that fits

Add senior ML engineers to your team, embed a dedicated team, or hand us the full build. Pick the model that fits how you work today.

Add ML engineers to your team

Senior nearshore ML engineers join your existing team, under your direction, in days, not months.

Embed a dedicated ML team

A stable, cohesive ML team that works only on your product as an extension of yours. You set priorities and roadmap, the team self-organizes sprint after sprint.

Hand us the full build

Hand us the roadmap and we deliver end to end, under our direction: data preparation, modeling, deployment, and monitoring. You get an outcome, not a team to manage.

Looking to hire by skill?

Hire nearshore developers →
OUR PROCESS

How we deliver machine learning, in your time zone

A four-step path from use case to a model that keeps its accuracy in production.

Discovery

We validate the use case, the data you have, and whether ML is even the right tool (30 minutes to start).

Data preparation

We collect, clean, and label your data so the model has something reliable to learn from.

Model building

We train and test against clear metrics, with you reviewing accuracy each sprint.

Deployment

We ship the model into production, then monitor and retrain it so accuracy holds as your data shifts.

FAQ

FAQs about our machine learning development services

01

How much do machine learning development services cost?
Most engagements start with a small, fixed-scope proof of concept, so you prove value before committing to a full build and upfront risk stays low. From there, cost tracks data readiness, model complexity, and how you engage the team. Senior LATAM rates land 30-50% under US equivalents, and every estimate is line-item, with no recruiting fees or hidden overhead.
02

How long until we have a working model?
It depends on data readiness and scope, but we work in short sprints toward a usable model, not an open-ended research project. Many engagements reach a first validated model in weeks, then improve from there against agreed metrics.
03

Do you build production machine learning, or just prototypes?
Production is the goal from day one. We can start with a proof of concept to prove value, but we design it to graduate into a deployed, monitored model, not a notebook that never ships.
04

Do we need a lot of clean, labeled data to start?
Not always. We assess your data early, and a large part of ML work is preparing, cleaning, and labeling it so the model performs. If data is thin, we tell you honestly and plan around it, including synthetic or third-party data where it makes sense.
05

What makes your team qualified for machine learning specifically?
Our engineers have trained and deployed real models (predictive, NLP, computer vision) into live products, not just built demos. You interview and approve every engineer, so you can confirm the ML experience yourself before anyone joins.
06

Which ML frameworks and tools do you work with?
The production-proven ones: TensorFlow, PyTorch, scikit-learn, and XGBoost for modeling, Hugging Face and spaCy for NLP, and MLflow, SageMaker, or Vertex AI for MLOps and deployment. We match the tools to your use case and budget.
07

Can you integrate the model into our existing product?
Yes, that is the common case. We wire the model into your product, APIs, and data pipelines, and work inside your tools as an extension of your team.
08

How do you keep model accuracy from degrading over time?
We monitor performance in production, set up evaluation and alerts, and retrain as your data shifts. Accuracy is tracked sprint by sprint, not assumed after launch.
09

Do you offer machine learning consulting, or only development?
Both. If you are still validating the use case, we start with ML consulting: feasibility, data assessment, and model strategy. When you are ready to build, the same team delivers it, so nothing is lost in a handoff.

Ready to put a model into production?

Tell us what you're building, and we'll show you the ML team that can build it.

Trusted by leading teams

  • Microsoft
  • Toyota
  • Panasonic
  • KPMG

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