Hire Machine Learning Developers

Machine learning development is not data science. A data scientist builds models in notebooks. A machine learning developer ships models to production -- serving infrastructure, monitoring, retraining pipelines, and integration with the rest of the stack. That combination is rare.
Most ML talent either has the research background or the engineering background, rarely both. RaftLabs ML developers have shipped production ML systems in healthcare, logistics, and financial services. They know what happens when a model degrades silently at 3am and nobody has monitoring -- because they built the monitoring to stop that from happening.

See our work

ML developers who have shipped production ML systems, not just notebook prototypes
Experience in healthcare, logistics, fintech, and retail ML use cases
Full MLOps coverage -- training, serving, monitoring, and retraining pipelines
Fixed-cost project engagements or dedicated team embedding

Recent outcomes

Voice AI · Research

Text-based interviews converted to automated phone calls

6× deeper insights

AI Automation · Ops

Manual invoice OCR across 40+ gas stations

20k+ txns day one

Loyalty · Retail

SuperValu & Centra loyalty platform with receipt validation

1,062 users in 4 weeks

SaaS · Logistics

Multi-carrier shipping hub for Indonesian eCommerce

2,000+ shipments yr 1

4.9 / 5 on ClutchSee all work

Recognition

Sound familiar?

Your data scientists built a model that works in notebooks but nobody can deploy it to production?
Looking for ML developers who understand both the model and the production engineering?

In short

Hiring machine learning developers through RaftLabs means accessing engineers who have shipped production ML systems across healthcare, logistics, fintech, and retail. RaftLabs ML developers cover the full pipeline: model training and evaluation, serving infrastructure, feature engineering, drift monitoring, and retraining. Engagements run as fixed-cost projects or dedicated team embedding. Start with a 2-3 week feasibility assessment before committing to a full build.

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The gap between notebook and production

Stack Overflow's 2023 developer survey found ML engineers command median salaries of $150,000+ in the US -- among the highest in software engineering. The demand is not for researchers who can train a model; it is for engineers who can take a trained model and make it run reliably at scale, in your infrastructure, for your users.

That combination of model knowledge and engineering discipline is rare. A researcher who learned ML in an academic or data science context knows models well but has rarely owned serving infrastructure, written the monitoring pipeline, or debugged a training-serving skew bug that silently corrupted predictions for three weeks before anyone noticed. An engineer who has not trained models struggles with the data science decisions that determine model quality.

Most failed ML deployments share the same pattern: a good model in a notebook, insufficient infrastructure around it, and no monitoring to catch degradation. The model works on the day it ships and erodes slowly until a business outcome fails and someone traces it back.

Dimension	Freelance ML developer	Data science agency	RaftLabs dedicated ML team
Production deployment experience	Variable	Typically data science focus	Full production ML pipeline
MLOps: serving, monitoring, retraining	Rarely included	Often out of scope	Standard part of every build
Regulated industry experience	Uncommon	Sometimes	Healthcare, fintech, logistics
Fixed-cost delivery	Rarely	Almost never	Yes, for scoped projects
Starts with feasibility assessment	Rarely	Varies	Always -- no build without data audit
Model monitoring after handover	Rarely	Typically not maintained	Included by default

Capabilities

ML engineering specialisms

ML model development

Engineers who design and train models for classification, regression, and anomaly detection using scikit-learn, XGBoost, LightGBM, and PyTorch. They start with a data audit before touching a model: volume, label quality, class balance, and feature coverage all affect what is possible. Model evaluation runs against stratified held-out sets with precision-recall analysis, calibration checks, and SHAP-based explainability for business sign-off. They have trained models for churn prediction, fraud scoring, demand forecasting, and operational anomaly detection in production environments.

MLOps engineers

Engineers who build the infrastructure that makes ML systems operable over time: model serving via FastAPI or BentoML with versioned endpoints and blue-green deployment; CI/CD pipelines for model training and deployment; experiment tracking and model versioning via MLflow or DVC; feature stores (Feast, Tecton) to eliminate training-serving skew; drift monitoring via Evidently AI or WhyLabs with defined alert thresholds; and automated retraining pipelines via Airflow or Prefect. Production ML without MLOps is a model that degrades silently until a business outcome fails.

Feature engineering specialists

Engineers who design the feature pipelines that determine model quality. Raw data is rarely in a form that a model can learn from directly. Feature engineering decisions -- lag features for time-series, interaction terms, aggregations over time windows, target encoding for high-cardinality categories -- often account for more accuracy improvement than model architecture choices. These engineers design feature stores that serve the same computed features during training and serving, preventing skew, and build the documentation that ensures features can be audited and reproduced.

NLP developers

Engineers who work on text classification, named entity recognition, sentiment analysis, and document understanding using Hugging Face Transformers, spaCy, and LLM-based approaches. They select between fine-tuned small models and LLM-based approaches based on your accuracy requirements, data volume, and latency constraints. Fine-tuned smaller models are cheaper and faster at inference; LLMs are more flexible for low-data or complex reasoning tasks. They have shipped NLP systems for document extraction, support ticket routing, compliance review, and customer feedback analysis.

Computer vision engineers

Engineers who build image classification, object detection, document OCR, and visual inspection systems. They work with PyTorch and ONNX for training and deployment, and have experience with both deep learning approaches (YOLO, ResNet, EfficientNet) and classical computer vision for simpler detection tasks. They have shipped vision systems for manufacturing quality inspection, medical image analysis, document processing, and retail shelf analysis. Production computer vision involves model quantisation for edge deployment, batching strategies for throughput, and confidence calibration for downstream decision systems.

Predictive analytics engineers

Engineers who build forecasting, churn prediction, demand planning, and credit risk models for business decision support. They translate a business question into a well-scoped ML problem, select the right approach (gradient boosting for tabular data with complex interactions, Prophet for seasonal time-series, LSTM for high-frequency sequential data), and deliver outputs that integrate into business workflows rather than sitting in a dashboard. Prediction intervals on forecasts, decision thresholds calibrated to your intervention budget, and output explanations for end users who need to act on predictions.

Have a model that needs to reach production?

Tell us the ML problem, what data you have, and what production looks like. We will assess feasibility and recommend the right approach before scoping a build.

Talk to our ML team

Process

How we scope and start ML engagements

Step 01
01
Assess the ML problem
We evaluate your use case, data, and production requirements before matching engineers or recommending an approach. This covers: what you are trying to predict or classify, what labelled data you have and in what volume, what the false positive and false negative costs are, and what production means -- real-time inference or batch scoring, what latency is acceptable, and what existing infrastructure the model must integrate with. Most ML failures are scoped wrong, not built wrong.
Step 02
02
Match by domain and stack
Healthcare ML is different from retail ML. A model that predicts patient readmission risk operates under HIPAA requirements, needs clinical explainability, and must handle class imbalance very differently from a churn prediction model. We match engineers by domain experience -- not just framework familiarity -- so the engineer working on your healthcare model has shipped healthcare ML, not just ML.
Step 03
03
Start with a scoped feasibility assessment
Before committing to a full build, we run a two-to-three week feasibility assessment: data audit, approach selection, baseline model evaluation, and a written recommendation. The assessment confirms the approach is sound and your data is sufficient before significant budget is committed. If the data is not ready, we tell you what needs to change. If the approach does not work on your data, you have spent three weeks finding that out rather than sixteen.

What clients say

What our clients say

Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.

Amer Abu Khajil

Canada

Founder, Peak Studios & Perceptional

I found RaftLabs to be the perfect partner for Perceptional, with their expertise in helping startup founders build MVPs, a free consultation, a prototype that matched my vision, and their unwavering support.

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Case studies

AI system for remote patient monitoring

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AI OCR for gas station operations

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Related services

Frequently asked questions

: A data scientist focuses on analysis, model selection, and experimentation -- work that typically lives in notebooks and produces insights or model files. A machine learning developer ships models to production: serving infrastructure, latency-optimised inference endpoints, drift monitoring, automated retraining pipelines, and integration with the rest of your stack. Both roles are valuable; most ML initiatives require both. If your model exists in a notebook but has not reached users, you need an ML developer, not more data science.
: Production stack includes scikit-learn, XGBoost, LightGBM, and CatBoost for tabular ML; PyTorch and TensorFlow for deep learning; Hugging Face Transformers for NLP and fine-tuning; FastAPI and BentoML for model serving; MLflow and DVC for experiment tracking and model versioning; Feast and Tecton for feature stores; Evidently AI and WhyLabs for drift monitoring; Apache Kafka for real-time feature pipelines; Airflow and Prefect for batch pipeline orchestration.
: Yes. Engineers work in your cloud accounts and deploy to your existing infrastructure. We have production experience with AWS SageMaker, GCP Vertex AI, and Azure Machine Learning for model training and serving, as well as self-managed deployments on Kubernetes for teams that want full infrastructure control. All code and configuration is in your repository and under your accounts from day one.
: A focused ML engagement -- feasibility assessment, data audit, model development, and production deployment with monitoring -- typically runs $30,000 to $100,000 depending on scope and data complexity. A full MLOps pipeline setup alongside model development runs $80,000 to $200,000. Dedicated ML developer embedding starts at $12,000 to $18,000 per month for a senior ML developer with part-time PM. We scope before pricing and deliver fixed-cost proposals, not hourly estimates.
: For a scoped feasibility assessment, we can typically start within two weeks of a signed agreement. The feasibility assessment runs two to three weeks and covers data quality evaluation, approach selection, and a clear recommendation before committing to a full build. This is the right starting point for most engagements -- it confirms the approach is sound before significant budget is committed.
: Yes. RaftLabs has shipped ML systems in healthcare (remote patient monitoring, clinical documentation), financial services (fraud detection, credit risk scoring), and logistics (demand forecasting, route optimisation). Engineers on regulated-industry engagements understand HIPAA data handling requirements, model explainability obligations for financial models, and audit trail requirements. Regulated-industry experience is not a checkbox; it changes how models are designed, evaluated, and documented.
: Production models degrade because the world changes. We deploy monitoring for two types of drift: data drift (input feature distributions shifting away from the training distribution, detected via statistical tests on held-out reference data) and model performance drift (prediction accuracy declining as ground truth labels accumulate). Monitoring runs via Evidently AI or Arize with defined alert thresholds. When drift crosses a threshold, we trigger a retraining pipeline -- retraining on a schedule without evidence is wasteful, but waiting for visible accuracy loss is expensive.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope Hire Machine Learning Developers in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.

Scope and cost agreed before work starts. No surprises. No obligation.
Working prototype within 3 weeks of kickoff.
Pay by milestone. You see progress before each invoice.
60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
All conversations are NDA-protected.