Do we need a huge dataset to start?

Not always. We assess what you have and whether it's enough for a useful first model, or whether a simpler approach makes more sense until you've collected more data.

How do you make sure the model actually works for our problem?

We validate against your real business metric, not just an academic accuracy score, and test on held-out data that reflects what production will actually look like.

Is this different from hiring a data scientist?

There's overlap, but ML engineers focus more on building and shipping models into working systems, while a data scientist may lean more toward analysis and exploration. We match based on what you actually need.

How fast can someone start?

We match from an existing vetted pool, so you're typically interviewing within days and can have someone contributing shortly after.

Contract or contract-to-hire?

Both. Many clients start on contract to prove fit on real work, then convert strong performers to permanent.

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Hire a Developer / AI & Agentic Engineering

Hire Machine Learning Engineers Who Can Build for Production.

Your model works in testing. Real-world data is where things get complicated. Hire vetted machine learning engineers who can build, optimize, deploy, and improve ML models that solve real business problems.

Book a Free ML Scoping Call→ See How We Fix It

★★★★★ 4.9/5 from 70+ clients 10+ YEARS 2,500+ PROJECTS

Sound Familiar?

Why Machine Learning Projects Stall After the Prototype

These are the problems teams face when they build ML without the right engineering expertise.

Your model performs well on training data but struggles with real-world inputs.

Your team spends weeks tuning models without a clear evaluation framework.

Data pipelines break and models train on incomplete or inconsistent data.

Nobody owns model deployment, versioning, or production monitoring.

Inference becomes slow and expensive as usage grows.

Your team builds a model but cannot turn it into a reliable product feature.

A strong model is only the starting point. Production machine learning requires engineering, evaluation, and continuous improvement.

Who This Is For

Built for teams turning machine learning into real products.

If you need specialized ML expertise to build, improve, or deploy models, this is for you.

Product Teams Adding Machine Learning

You have a clear use case but need an engineer who can turn data and models into a working product feature.

Companies Scaling Existing ML Models

Your model works, but accuracy, latency, or infrastructure costs become problems at scale. You need an engineer who can optimize the system.

Data & Engineering Teams That Need ML Expertise

Your developers understand the product but lack deep experience with model development, feature engineering, experimentation, or ML deployment.

How We Fix It

We Vet for Machine Learning Depth, Not Just Framework Knowledge.

We test how engineers develop, evaluate, optimize, and deploy machine learning systems.

Fixes: Weak or inconsistent model performance

Model Development & Optimization

We test experience with model selection, feature engineering, hyperparameter tuning, and performance optimization.

Fixes: Poor data quality and unreliable predictions

Data & Feature Engineering

We test how engineers build reliable datasets, feature pipelines, and preprocessing workflows for production models.

Fixes: Models that look good but fail in production

Model Evaluation

We test how engineers select metrics, build validation strategies, handle edge cases, and detect model performance issues.

Fixes: Models stuck in notebooks

Production ML Deployment

We test experience deploying models through APIs, batch pipelines, containers, and scalable inference infrastructure.

Fixes: Slow and expensive ML systems

Model Optimization

We test how engineers reduce inference latency, memory usage, and compute costs without sacrificing model quality.

Fixes: Hiring the wrong ML profile

ML System Design

We test how engineers design complete ML workflows from data ingestion through training, deployment, monitoring, and iteration.

Why CrecenTech

We vet engineers for the ML problems that matter in production.

We go beyond resumes and framework lists. Our vetting looks at how engineers approach model performance, data quality, deployment, scalability, and failure modes. We match engineers based on the actual ML problem you need to solve.

Book a Free ML Scoping Call →

Real ML Experience Engineers who build models for real applications.

Scope Before Staffing We define the right ML skill set before matching talent.

Production Over Demos We test engineers on practical ML challenges.

Flexible Engagements Hire on contract, contract-to-hire, or project basis.

10+ Years Experience Trusted by 70+ businesses across 2,500+ projects.

The Honest Comparison

Build it yourself, hire a generalist, or match with CrecenTech.

The right ML expertise can determine your model's accuracy, reliability, cost, and time to production.

DIY In-House

On your own

Scoping the Right ML Solution

✕Your team research models and approaches while managing its existing roadmap.

Model Performance

✕Trial and error can slow progress without specialized ML expertise.

Production Readiness

✕Models may work in notebooks but lack reliable deployment workflows.

Time to Value

✕Your team learns ML engineering while building the product.

Generalist Developer

Research-focused

Scoping the Right ML Solution

–They may build the requested model without challenging the technical approach.

Model Performance

–Basic implementation may not cover tuning, evaluation, or edge cases.

Production Readiness

–They can build the model but may lack deep ML deployment experience.

Time to Value

–Development can start quickly, but ML depth varies.

Recommended

CrecenTech

Scoping the Right ML Solution

✓We define the problem and match the right ML specialization./span>

Model Performance

✓We build with evaluation, optimization, and real-world data in mind.

Production Readiness

✓Engineers design for deployment, scale, monitoring, and iteration.

Time to Value

✓Start with vetted ML talent and move quickly toward a useful production feature.

How It Works

From ML requirements to production-ready model.

01

Technical Scoping Call

We review your data, use case, current stack, model requirements, and success metrics.

02

Talent Matching

We match you with ML engineers based on your model, data, and engineering requirements.

03

Code & System Design Review

Shortlisted engineers complete a relevant ML assessment before you interview.

04

Trial Task Option

Start with a paid micro-engagement to validate technical fit before full commitment.

What You Get

Everything you need to build machine learning into your product.

ML Feature Scoping

Vetted ML Engineer

Model Development

ML Deployment Support

Flexible Engagements

Replacement Guarantee

★★★★★

They gave us a custom-built solution which makes it much easier to keep track of our data and act on it.

Blue Halo Homes

AI-Fluent Engineering · CrecenTech client

FAQs

How does CrecenTech vet ML engineers? +

We verify production deployments via GitHub reviews and system design interviews. Tutorial projects are disqualified. Only candidates with live ML infrastructure experience pass screening.

What if the ML engineer isn’t a fit? +

We replace them at no cost within 90 days. A mandatory 2-week knowledge transfer overlap ensures zero downtime and complete documentation handoff.

ML engineer vs AI developer: what’s the difference? +

ML engineers focus on model training, feature engineering, and optimization. AI developers build LLM apps, RAG systems, and agents. We scope which role solves your bottleneck.

Can they integrate with our existing data stack? +

Yes. They harden existing pipelines without rip-and-replace migrations. Full runbooks ensure your team retains operational control post-engagement.

Do candidates have production-scale ML experience? +

Yes. Vetting requires proof of handling real-world data drift, optimizing inference latency, managing compute costs, and building evaluation frameworks. Sandbox-only experience is disqualified.

Hire a Machine Learning Engineer Who Can Build Beyond the Model.

Need more than a model that works in a notebook? Get vetted ML talent that can build, optimize, and deploy
production-ready machine learning systems.

Get Vetted ML Candidates in 48hrs Browse All Skills