Python · PyTorch · scikit-learn · MLflow
Hire ML engineers
Vetted machine learning engineers who train, deploy and maintain models on your data, from predictive and ranking models to computer vision and NLP, matched to your use case and budget. On your team in 2–3 weeks.
Rated 5.0 on Clutch · CVs in 3–5 days · No long-term lock-in
- Python
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face
- MLflow
What does an ML engineer do?
A machine learning (ML) engineer trains, deploys and maintains custom models built on your own data: predictive models, recommendation and ranking, computer vision and NLP. They own the full model lifecycle, from data preparation and feature engineering to training pipelines, serving and monitoring for drift, so models stay accurate after launch.
DevKit ML engineers work in Python with PyTorch, TensorFlow, scikit-learn, XGBoost and Hugging Face, and MLOps tools such as MLflow, Kubeflow, SageMaker and Vertex AI. Every one of them passes a technical interview with our senior engineers before you see their CV.
When to hire one
- Training prediction, classification or forecasting models on your data
- Building recommendation and ranking systems
- Computer vision and NLP models tuned to your domain
- Setting up MLOps: training pipelines, model registry and monitoring
What our ML engineers do
Typical work our ML engineers take on for clients.
Predictive models
Classification, regression and forecasting models for churn, risk, demand and pricing, trained on your data.
Recommendation and ranking
Recommenders, search ranking and personalisation models with offline evaluation and A/B testing.
Computer vision
Image classification, object detection and segmentation models built and fine-tuned with PyTorch.
NLP and deep learning
Text classification, entity extraction and fine-tuned transformer models with Hugging Face.
Data and feature engineering
Data preparation, feature pipelines and labelling workflows in pandas, SQL and Spark.
MLOps and monitoring
Training pipelines, model registry, serving and drift monitoring with MLflow, Kubeflow, SageMaker or Vertex AI.
ML technologies we cover
Languages and ML frameworks
- Python
- PyTorch
- TensorFlow
- Keras
- scikit-learn
- XGBoost
- Hugging Face
Data processing
- pandas
- NumPy
- SQL
- Spark
- Airflow
MLOps
- MLflow
- Kubeflow
- Docker
- Kubernetes
- Weights & Biases
Cloud ML platforms
- AWS SageMaker
- Google Vertex AI
- Azure ML
- Databricks
Matched to your budget: which level do you need?
We match the level to the work, so you don't pay senior rates for tasks a mid-level engineer handles well, or put critical work on the wrong hire.
Junior
Well-defined tasks with guidance
Bug fixes, tests and smaller features in an established codebase, with a senior on your team to review their work. The most budget-friendly option.
Mid-level
Independent feature work
Builds features end to end in an existing architecture with little supervision. The best value for most product teams.
Senior
Complex features and ownership
Owns complex parts of the system, makes sound technical decisions and mentors others. Worth it where mistakes are expensive.
Tech lead / Architect
Architecture and team leadership
Designs the system, sets standards and leads other engineers. Ideal for new products, rewrites or a team without technical leadership.
Not sure? Tell us the work and your budget on a free call, and we'll recommend the right level for each role.
What's in every profile we send
Within 3–5 business days of our call, you get a shortlist of vetted candidates. Every profile gives you what you need to decide quickly.
- 01
Skills verified by engineers
Each candidate passes a technical interview with our senior engineers. You see what they can actually do, not just what their CV says.
- 02
Interviewer's notes
Strengths, gaps and how the candidate approached the interview tasks, written by the engineer who interviewed them.
- 03
Why this match
How the person fits your role, your team and your budget, and which level we recommend for the work.
- 04
Level and relevant experience
From junior to tech lead, with the projects and domains that matter for your role.
- 05
English, time zone and availability
Communication level, working-hours overlap with your team and the earliest start date.
- 06
A clear monthly rate
One price per engineer per month, so you can compare candidates against your budget straight away.
Client reviews: rated 5.0 on Clutch
Verified Clutch reviews from engineering leaders and founders who scaled their teams with DevKit.
Head of Engineering, IT Company
“Their flexibility and ability to provide access to talented specialists.”
Read on ClutchLead Frontend Engineer, Ultra Commerce
“We were particularly impressed by their management approach.”
Read on ClutchSenior Unity Engineer, Software Company
“Their management side always cared about the engineers provided.”
Read on ClutchCEO, Software & AI Company
“Their responsiveness and the quality of the engineers were impressive.”
Read on ClutchDevOps Engineer, Ubiqube
“They were always ahead of schedule and suggested a lot of modern solutions.”
Read on ClutchSenior Software Engineer, HealthTech Firm
“They combine technical expertise with a strong understanding of business objectives.”
Read on ClutchTeam Lead, AI Solutions Co
“DevKit has found an approach that works well with startups.”
Read on ClutchThree ways to hire
Choose how you want to work. You can switch models as your needs change.
Hiring ML engineers: FAQ
How quickly can I hire an ML engineer through DevKit?
You usually receive vetted ML engineer CVs within 3–5 business days, and the engineer typically starts within 2–3 weeks of the first call.
Which ML tools do your engineers use?
Mainly Python with PyTorch, TensorFlow, scikit-learn, XGBoost and Hugging Face for modelling, pandas, SQL and Spark for data, and MLflow, Kubeflow, AWS SageMaker or Google Vertex AI for training pipelines, deployment and monitoring.
What is the difference between an AI engineer and an ML engineer?
An ML engineer trains, deploys and maintains custom machine learning models on your own data. An AI engineer builds product features on top of existing foundation models such as LLMs, using prompts, RAG and agents. If you need a predictive, ranking or computer vision model, hire an ML engineer; if you need an LLM feature or AI agent, see our AI engineers.
Do I need a senior ML engineer?
Not always. If your models and pipelines are in place and someone senior reviews the work, a mid-level engineer often delivers the same results for less, for example retraining models, adding features or maintaining pipelines. To choose modelling approaches, design an MLOps platform or put models into critical decisions, a senior engineer or tech lead is usually worth it. We'll recommend the right level on a free call.
How much does it cost to hire an ML engineer?
Staff augmentation is billed as a monthly rate per engineer, depending on the level and specialisation. Tell us your budget and we'll match ML engineers to it; exact rates come with the first CVs.
Need engineers next month? Let's talk this week.
Send us the role. Get CVs in 3–5 days.
Tell us what you need. We reply within one business day.
What happens next
- 1
We reply within one business day
A real person reads every request, not a bot.
- 2
A free 30-minute call
We talk through the role, your team, the timeline and the budget.
- 3
Vetted CVs in 3–5 days
Each profile comes with interview notes and a clear monthly rate.
“Their responsiveness and the quality of the engineers were impressive.”
hi@devkit.agency