Álvaro
Data Scientist
“In a climate this uncertain, Shakers gives you a layer of reassurance and confidence. What I like most, without a doubt, is the flexibility and getting to work on top-tier projects.”
Join ShakersShakers for expert talent
Build your career as an MLOps Engineer
Projects where operating the model is the engagement: how it trains, how it deploys and what gets watched when the data changes. Shakers connects you with teams that have the model trained and need it to keep serving.
MLOps employment in Spain is full-time consultancy and industry: Amaris, TECDATA, Airbus or METRICA title the role in English. The route of operating models by project barely had a local door.
This is not a training route: it is what gets operated on a real engagement, what you must prove and where your territory ends.
- Higher-paying projects
- More continuity between projects
- Greater recognition for your experience
+3,000
Tech projects published on Shakers
+90
Tech, Product and Data specialisms are part of Shakers
6 months
Average length of a project with Shakers
4.7/5
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Amelia
AI Agent Developer
“I have been building AI agents for years. Thanks to Shakers I stopped chasing clients and started genuinely choosing which projects I wanted to work on. I work with some of the best companies in Europe, at my own rate, and I focus on what I am good at and what I love: building.”
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Luz
Backend Developer & Data Engineer
“Shakers adds a lot of value because it connects you with sharper projects, already filtered and better defined. It saves you a big part of the upfront work of understanding the client, quoting and weighing up whether it is worth it, and that means that as a contractor you can go far more directly to the opportunities that genuinely fit you.”
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Rubén
Software Developer
“Life as a contractor has a lot going on and it is not always easy. We like to focus on what we are good at, but you also have to manage clients, invoicing and go looking for work. For me, Shakers has been key because it does that prospecting for me, it gives the client confidence from the very first moment, and it lets us be part of a community with real enthusiasm.”
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Alejandro
AI Developer
“Shakers was key to getting my company off the ground: it gave me the flexibility to keep contracting while I built my next chapter. Without Shakers, I would have had to raise funding or push the project back.”
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Toño
Product Designer
“With Shakers I have landed recurring projects that give me peace of mind and room to grow professionally. It is not just about reaching quality clients: it is feeling that you have a network behind you, one that connects you with real opportunities and helps you move forward as a freelancer.”
Join ShakersWhat an MLOps Engineer operates every week
An MLOps Engineer industrialises machine learning models: builds the training and deployment pipelines, versions data and experiments, automates model CI/CD and monitors drift to retrain before quality falls. The deliverable is the model operating, not the model trained.
Their boundary: DevOps ships applications that hold still until the next release; MLOps operates models that degrade when the data changes. The ML Engineer trains the model; MLOps keeps it serving.
You automate the model's path from commit to production, version data and experiments in MLflow, and watch drift with thresholds that decide retraining. You also decide what does not get automated: an alert nobody attends gets switched off with an argument. Someone else trained the model; keeping it serving is yours.
PythonDockerKubernetesCI/CDMLflowKubeflowVertex AISageMakerAzure MLAirflowDatabricksTerraformKafkaGitDrift monitoring
Model CI/CDExperiment versioningMLflowDrift monitoringRetrainingKubeflow
Free to join, no hidden fees
How Shakers works for talent
Create your profile
Tell us who you are, what you can do, the projects you want to work on and what your availability and rate are.
Get your certification
We certify your experience and your use of AI so companies understand and trust what you bring.
Match with projects
We connect you with well-paid projects that fit your expertise and your preferences.
Projects that live up to your expectations
Work on real projects, with teams that need your expertise, without wasting time looking for opportunities.
Projects that fit you
Work with companies that need your stack and experience to take their projects to production.
Opportunities that come to you
Our AI matching connects your profile with the projects that fit your preferences.
You build. Shakers handles the rest
Set your own terms based on your seniority. We manage contracts, payments and paperwork.
What an MLOps Engineer delivers at Shakers
The engagement arrives with a live model
The trained model that expires, the pipeline that does not exist and the drift nobody watches: an operations problem, not a list of tools.
From commit to production
Automated training and deployment pipeline, experiments versioned in MLflow and drift alerts with an owner. Operations the team understands.
The platform you already run
Kubernetes, CI/CD and an ML platform: Vertex AI, SageMaker or Azure ML. The tool is your infrastructure's, not the fashionable one.
Counted pipelines, not courses
Having industrialised real models and knowing how to tell it: what you automated, which drift you caught and how many retrainings ran clean.
Automating is also deciding
Choosing what gets automated and what stays watched by hand: an alert nobody attends gets switched off. Stable operations are judgement.
Where your engagement ends
You operate the model; training it belongs to the ML Engineer and the wrapping application to the DevOps. If the engagement crosses, their row joins.
Frequently asked questions
Any other questions? Write to us and we will reply.
What does an MLOps Engineer do by project?
They put models in production and keep them useful: pipelines, CI/CD, experiment versioning, drift and retraining. One concrete model, one period and thresholds, with no fixed payroll.
What is the difference between MLOps and DevOps?
The artefact: DevOps ships applications; MLOps operates models that degrade when the data changes. If you come from DevOps, the stack you already own, and the artefact changes your craft.
How do MLOps and LLMOps relate?
LLMOps is the same craft on foundation models: evals, RAG in production and gateways. Employment already asks for them together ('MLOps and LLMOps'); the specialisation adds territory.
How much to charge as a contract MLOps Engineer?
The mechanism marks it: seniority, cloud or own infrastructure, full pipeline or audit, and days against project. Visible rates belong to another market; your budget closes per engagement.
Is there enough engagement in Spain for the role?
Employment sends the signal: 992 active tech ads in Spain mention MLOps and Infojobs lists 40, with literal titles in consultancy and industry (Shakers market analysis, n=31,957, September 2026).
How do I get in without a contact network?
By getting certified: the process certifies your practice and the collective brings the engagements. Your work is showing pipelines operated with real consequences; matching projects is ours.
The daily shape of the craft
The pipeline that breaks with every release, the experiment nobody can find and the drift the business detects before the platform does. Less heroics, more models operated with traceability.
The title is English and the craft is literal
992 active tech ads in Spain mention MLOps, with literal titles at Amaris, TECDATA or Airbus (Shakers market analysis, n=31,957, September 2026). Your engagement, keeping the model serving.
From DevOps to MLOps without starting over
Kubernetes, Terraform and CI/CD are already yours if you come from DevOps; the new part is the artefact: data that changes, models that degrade, experiments to version. The other half gets learned operating.
The LLMOps layer is already here
Evals, RAG in production and gateways: operating foundation models enters the same engagement, adding territory to the craft, not splitting it.
Who you share production with
A model in production does not live alone: whoever ships the app and whoever channels the data cross your path in every engagement.
DevOps Engineer
The direct boundary: same stack, different artefact. If the engagement ends in an application and not a model, their row takes the containerised delivery.
SRE
The operation that holds traffic: when the model serves and the platform is what fails, their row builds the SLOs and the incident response.
Platform Engineer
The self-service layer your pipelines lean on: when the team grows, their row builds the internal platform that holds them.
Data Engineer
The channel that feeds retraining: Airflow, dbt and the history ready. Without versioned data, the drift you watch has no cause to trace.
AI Engineer
The 2026 fusion point: evals and RAG in production are LLMOps and their territory at once. When the engagement crosses into generative, their row joins.
Boundaries between these roles get agreed per project: the job title does not set them.
Take the next step with Shakers
Want to operate models that hold up?
Pass Shakers certification and choose which production gets your craft.
