Hire machine learning engineers who take models into production
Remote machine learning engineers who turn data and models into working software: pipelines, training, deployment and monitoring. Hire one into your team, interview them first, and own everything they build.
$45 to $60 an hour
Machine learning engineers on our AI engineer rates, or £36 to £48 an hour in the UK.
Overlap with your day
Four hours a day with UK teams at no extra cost, or three with US Eastern on an evening shift.
Two-week trial
No charge if you stop within the first five working days.
What does a machine learning engineer do?
A machine learning engineer turns models into reliable software. They build data pipelines, train and evaluate models, package them behind APIs, and monitor them once they are live. A data scientist asks whether a model can work. The ML engineer makes it work every day, at the speed and cost the product needs.
Machine learning covers more than language models. It includes forecasting, recommendations, fraud and anomaly detection, image recognition, and classifying text or records. Some products combine the two, such as a classifier that routes tickets before an LLM drafts the reply.
- Data pipelines that collect, clean and version training data.
- Feature engineering and training code that runs the same way every time.
- Evaluation against a fixed test set, with results recorded for each model version.
- Packaging a model behind an API or a batch job, at a latency and cost the product can live with.
- Monitoring for data drift and falling accuracy after launch.
- Retraining schedules, rollbacks and documentation your team can follow.
Webify.AI bills machine learning engineers on its AI engineer rates. That is $45 an hour for an AI / LLM engineer, and $60 for a senior AI engineer who leads the design. Our engineers work mainly in Python. Tell us your models and tools, and we put forward engineers whose experience matches them. If we don't have a match, we'll tell you.
What is the difference between an AI engineer and a data scientist?
A data scientist studies data and builds models to answer questions and test ideas. An AI engineer builds products on models, now often large language models, and makes them reliable in production. A machine learning engineer sits between them, training models and shipping them. Hire for the output you need: insight, a model or a feature.
Swipe the table sideways to see every column.
| Data scientist | Machine learning engineer | AI engineer | |
|---|---|---|---|
| Main question | What does the data tell us? Could a model help? | How do we train this model and run it reliably? | How do we build a product feature on a model? |
| Typical output | Analysis, experiments, prototype models and reports | Training pipelines, deployed models and monitoring | LLM features, AI agents, RAG systems and integrations |
| Core skills | Statistics, experiment design, SQL and Python | Software engineering, Python, model training and deployment | Software engineering, model APIs, retrieval and evaluation |
| Common tools | Notebooks, pandas, scikit-learn and BI tools | PyTorch or TensorFlow, MLflow, Docker and cloud ML platforms | Model APIs, open-weight models, vector databases and agent frameworks |
| Hire when | You have data and questions, but no clear model yet | You have a model idea that must run in production | You want AI features in your product |
Tools are examples common across the industry. Job titles overlap, and many people cover two of these columns.
If you are unsure which role you need, start from the output. A report or a decision needs a data scientist. A model running inside your product needs an ML engineer. A feature built on an LLM needs an AI engineer or an LLM engineer.
Webify.AI puts forward engineers for the last two columns. If your main need is statistical analysis, we will tell you plainly whether we can match it.
Our rates
Published prices in US dollars. Monthly plans are 160 hours, about 15% below the hourly rate.
Mid-level developer
3 to 5 years
$25 / hour
$3,400 a month (160 h)
Senior developer
6 or more years
$35 / hour
$4,750 a month (160 h)
AI / LLM engineer
Agents, RAG, IBM watsonx
$45 / hour
$6,100 a month (160 h)
Senior AI engineer
Leads AI architecture and delivery
$60 / hour
$8,150 a month (160 h)
Two-week trial with the developer you choose. No charge if you stop within the first five working days. US Eastern clients get 09:00 to 12:00 US Eastern time through an evening shift, at 15% extra. Full pricing and comparisons.
What is MLOps and why do I need an MLOps engineer?
MLOps is the practice of running machine learning models reliably in production. It covers versioned data and models, automated training and tests, repeatable deployment and live monitoring. You need an MLOps engineer when models retrain often, several run at once, or a silent accuracy drop would cost money.
- Reproducibility. Any model in production can be rebuilt from versioned code, data and settings.
- Automation. Training, testing and deployment run as a pipeline, not as steps someone has to remember.
- Safe releases. New models go out gradually, with a quick rollback if results get worse.
- Monitoring. Alerts when input data drifts, predictions shift or latency climbs.
- Cost control. Compute sized to the job, and idle GPUs switched off.
- Traceability. A record of which model version made which prediction, for audits and debugging.
A single model retrained twice a year may not need a dedicated MLOps engineer. A machine learning engineer can set up the basics. Once you run several models, or retrain weekly, MLOps becomes a job of its own.
Common MLOps tools include MLflow and Weights & Biases for experiment tracking, and Airflow or Kubeflow for pipelines. The large cloud providers also offer their own ML services. We work with the tools you already use.
What is LLMOps, and how is it different from MLOps?
LLMOps applies MLOps ideas to systems built on large language models. You rarely train the model yourself. Instead the work is versioning prompts, evaluating outputs, tracking token cost, tracing multi-step calls and handling model updates. An MLOps engineer can learn it, but the tests and failure modes are different.
| MLOps | LLMOps | |
|---|---|---|
| What changes most often | Training data and model weights | Prompts, retrieval settings and the provider's model version |
| How quality is measured | Accuracy and similar metrics on a test set with known answers | Scored outputs on a test set, often with human review or a second model as judge |
| Main cost | Compute for training and serving | Tokens per request, or GPUs for self-hosted models |
| Typical failure | Data drift lowers accuracy over time | A prompt or model change quietly breaks answers, or prompt injection |
| What to monitor | Input distributions, prediction drift and latency | Traces of each step, cost per request, refusals and user feedback |
LLMOps matters for every RAG system and AI agent you run. The engineer who builds them should set up the basics from the first week: a test set, cost tracking and traces.
How much does a machine learning engineer cost?
A machine learning engineer from Webify.AI costs $45 an hour, or $60 for a senior AI engineer. Monthly plans of 160 hours cost $6,100 and $8,150. UK rates are £36 and £48 an hour. For comparison, KORE1 puts US AI and ML engineers hired through staffing agencies at $140 to $240 an hour.
| Option | Rate | Source |
|---|---|---|
| Webify.AI AI / LLM engineer | $45 an hour, or $6,100 a month for 160 hours | Our published rates |
| Webify.AI senior AI engineer | $60 an hour, or $8,150 a month for 160 hours | Our published rates |
| Webify.AI, UK clients | £36 an hour (£288 a day) or £48 an hour (£384 a day) | Our published rates |
| US AI / ML engineer through a staffing agency | $140 to $240 an hour | KORE1, 2026 |
| Senior software developer in Asia | $31 to $41 an hour | Accelerance, 2026 |
| Senior software developer in Latin America | $60 to $75 an hour | Accelerance, 2026 |
| Senior software developer in Central and Eastern Europe | $64 to $76 an hour | Accelerance, 2026 |
| UK contract software developer | Median £525 a day | IT Jobs Watch, October 2026 |
Market figures are published ranges, not quotes. Only the KORE1 figure is specific to AI and ML engineers. The others cover software developers in general, and ML specialists may cost more.
Sources: KORE1 tech contractor rates 2026, Accelerance 2026 outsourcing rate trends and IT Jobs Watch UK contract rates. Our own rates, with monthly plans, are on the pricing page.
The hourly rate is only part of the machine learning engineer cost. Compute for training and serving runs in your own cloud account. Data annotation, if you need it, is a separate cost too. A good engineer will estimate both before the work starts.
Can remote or offshore machine learning engineers work on sensitive data?
Yes, with the right setup. Offshore machine learning engineers can work inside your cloud account, under your access controls, on data that stays in your region. Where rules require it, they can work on de-identified or synthetic samples instead. Agree what data they may see before they start, and give access through your own accounts.
Hiring remote machine learning engineers works well for most ML tasks, because code, experiments and deployments all live in shared systems. Settle three questions first: data, compute and hours.
- Data access. Decide what the engineer may see. Remote access from another country can count as an international data transfer under laws such as UK GDPR, so involve whoever handles data protection.
- Compute. Training runs in your cloud account or on your servers, so you keep control of the data and the bill.
- Hours. UK teams get 09:00 to 13:00 UK time every working day, inside a normal Indian working day, at no extra cost. US teams get 09:00 to 12:00 US Eastern time through an evening shift, at 15% extra.
- Written records. Experiment notes and short model descriptions, so decisions survive the time difference.
If your engineer will handle personal data, we agree data-protection terms with you before they start. Webify.AI is headquartered in Ahmedabad, India, and its engineers work from there.
For the wider case for offshore AI teams, see offshore AI development. Country detail is on our pages for UK companies and US companies.
How do you test a machine learning engineer in a two-week trial?
Give them a real, small task with a clear finish line. Good choices are reproducing an existing training run, adding experiment tracking, writing data validation checks, or deploying a model to a staging API. Watch how they document, ask questions and handle surprises. If it is not working, stop within five working days at no charge.
- 1
Before day one
Pick the trial task, prepare access to a copy of the data, and write down what done looks like.
- 2
Days 1 and 2
The engineer sets up the environment, runs the existing code, and notes anything that doesn't reproduce.
- 3
Days 3 to 5
They work on the task and open their first pull requests for your review. This is your first decision point: stop now and there is no charge.
- 4
Week 2
They finish the task, document it and propose the next step. You continue hourly, move to a monthly plan, or ask for a different engineer.
In the interview, ask about a model that failed in production and what they changed afterwards. How someone handled a failure says more than a list of frameworks.
Which tools do machine learning engineers typically use?
Most machine learning engineers work in Python. Typical data tools are pandas and SQL. Models are built with scikit-learn, XGBoost, PyTorch or TensorFlow. MLflow tracks experiments, Docker packages models, and a cloud ML platform runs training and serving. Tell us your stack and we put forward engineers whose experience matches it.
| Job | Common tools (examples) |
|---|---|
| Data preparation | SQL, pandas, PostgreSQL, and Spark for large datasets |
| Classic models | scikit-learn, XGBoost, LightGBM |
| Deep learning | PyTorch, TensorFlow, Hugging Face Transformers |
| Experiment tracking | MLflow, Weights & Biases |
| Pipelines | Airflow, Kubeflow, Prefect, Dagster |
| Serving | FastAPI, Docker, Kubernetes, or managed endpoints on a cloud ML platform |
| Cloud ML platforms | Amazon SageMaker, Google Vertex AI, Azure Machine Learning, IBM watsonx |
Examples of tools common across the industry, not a list of Webify.AI certifications or partnerships. Webify.AI is an IBM Partner and deploys on IBM watsonx.
Test tool experience in the interview rather than trusting a list. Ask the engineer to explain a choice they made with a tool, and what they would do differently now.
If you are adding several AI roles, see AI staff augmentation for how a mixed team of AI and software engineers works.
Frequently asked questions
Can one engineer cover both machine learning and MLOps?
On a small team, often yes. One engineer can train models and set up basic tracking, deployment and monitoring. As the number of models grows, splitting the roles keeps releases safe and the work manageable.
Do you provide data scientists?
Our engineers focus on building and running models and AI features in production. If your main need is statistics and experiments, tell us what you need. We will say plainly whether we can put forward a matching profile.
Where does training compute run, and who pays for it?
In your cloud account or on your own servers, so your provider bills you for compute directly. That keeps your data and models under your control. Your engineer can size the resources and switch off what is idle.
Can we hire a machine learning engineer for a short project?
Yes. Hourly plans start at 80 hours a month. That suits a defined piece of work, such as setting up a training pipeline or deploying one model. Move to a monthly plan if the work keeps going.
Who owns the trained models and pipelines?
You do. Code, pipelines and configuration live in your repositories, models are stored in your accounts, and the IP is assigned to you.
How do teams on the US West Coast work with an engineer in India?
They can use the same evening-shift hours, which are 06:00 to 09:00 Pacific, at 15% extra. Or they can work asynchronously, with a written handover at the start of the engineer's day.
How do you check a model for bias?
Bias checks belong in evaluation. The engineer breaks test results down by the groups that matter for your use case, and flags gaps before release. Decisions on acceptable trade-offs, and any legal duties, stay with you.
Can an ML engineer improve a model we already have?
Yes. A common starting point is to reproduce the current model, measure it on a fixed test set, then try changes one at a time. Without that baseline, nobody can tell whether a new version is better.
Can an ML engineer help us decide whether we need machine learning at all?
Yes, and it is a good first task. Sometimes a rule, a SQL query or an off-the-shelf model does the job. A good engineer will say so, which saves you building a training pipeline you don't need.
How is an ML engineer different from an LLM engineer?
An ML engineer trains and deploys models, often on your own data. An LLM engineer mostly builds on existing language models through prompts, retrieval and tool calls. The skills overlap, and many projects need some of both.
Ask for two machine learning engineer profiles
Tell us the role and when you need someone. We'll email two named engineers with their experience and availability within 48 hours, and you choose who to interview.
- No cost to receive profiles
- Two-week trial with the developer you choose. No charge if you stop within the first five working days.
- Invoiced in USD or GBP; IP assigned to you.