Mateo
Full Stack Developer
ReactNode.jsPythonTypeScriptNext.jsMongoDBAWSAIAPIs
Shakers for companies
Work with a contract MLOps Engineer
A certified MLOps Engineer for the model that works today and expires tomorrow: training and deployment pipelines, data and experiment versioning, model CI/CD, and drift that triggers retraining.
Software does not age until someone changes it; models decay when the data changes. One specific person, with a budget set by scope.
- Certified talent in AI and agent skills
- Meet up to 3 AI Builders in 72 h
- Flexible collaboration models
+600
European clients across different industries
Top 4%
We work with the top 4% of certified talent in our community
72 hours
Meet 3 certified candidates in under 72 h
4.7/5
Our clients' satisfaction on Trustpilot
The talent mix your company needs
At Shakers you will find AI Engineers and specialists across Tech, Product and Data
Ana
DevOps Engineer
DockerCI/CDAWSAzureKubernetesTerraformAmazon Bedrock
Lucas
Backend Developer
Node.jsPythonJavaGoPHPMySQLMongoDBDocker
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Emma
Security Engineer
Burp SuiteOWASP ZAPMetasploitNmapNessusTrivySnyk
What this profile brings to your project
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.
PythonDockerKubernetesCI/CDMLflowKubeflowVertex AISageMakerAzure MLAirflowDatabricksTerraformKafkaGitDrift monitoring
One provider. One contract. Full traceability
How Shakers works for your company
Post your project
Tell us about the model you need to operate, the infrastructure it runs on and when you want to start.
You get three options
We match your brief against certified talent and introduce the three AI Builders who fit best.
The last word is yours
Meet the three specialists and choose who you collaborate with; we handle the rest.
Why work with Shakers
The advantages of adding this profile with Shakers
The model that does not expire
Software does not decay on its own; models do. Drift monitoring, retraining and watched metrics: the prototype keeps answering.
Training-to-deployment pipelines
Training, validation and deployment in one automated flow with CI/CD, Docker and Kubernetes: every version ships traced.
Data and experiments versioned
MLflow and a registry with lineage: which data trained which version and who deployed it. The audit stops being archaeology.
On-premise when data cannot leave
Real-time models on your own infrastructure: an architecture that respects your constraint where cloud platforms do not go.
The LLMOps layer included
The same craft on foundation models: evals, RAG in production and gateways, ready when the LLM enters your product.
Continuity if the specialist moves on
If the specialist moves on, the operation does not stop: documented pipelines, alerts with an owner and a replacement.
Frequently asked questions
Any other questions? Our team will answer them.
Tell us about your pipelineWhat does a contract MLOps Engineer do?
They put models in production and keep them useful: training and deployment pipelines, CI/CD, experiment versioning, drift and retraining. By project, on your cloud or your own infrastructure.
What is the difference between MLOps and DevOps?
The artefact: DevOps ships applications that hold still until the next release; MLOps operates models that degrade when the data changes. App pain, DevOps; a model that expires, MLOps.
Do I need an MLOps or an ML Engineer?
The ML Engineer trains the model; the MLOps industrialises it and keeps it serving. Ads fuse them into dual titles; in the engagement, the boundary gets written first.
How do MLOps and LLMOps relate?
LLMOps is the same craft on foundation models: quality evals, RAG in production and gateways. Operating LLMs enters as an MLOps specialisation, not as a separate role.
How much does a contract MLOps Engineer cost?
The mechanism sets it, not a rate: seniority, cloud or own infrastructure, full pipeline or audit, and days against project. Visible rates belong to another market; the budget closes per engagement.
How do I check they really operate models?
By counted pipelines: automated deployments, drift detected and retrainings run, not just Kubernetes. Certification here verifies that practice on real cases.
What gets delivered in the first weeks
The map of the model in production: how it trains, how it deploys, what gets watched and what is missing. The first deliverable arrives with the retraining pipeline and the drift panel that warns before the business feels the decay.
A craft the market already titles
992 active tech ads in Spain mention MLOps, with 40 listings on Infojobs and literal titles such as 'MLOps Engineer' at Amaris or TECDATA and 'Ingeniero de MLOps' at Apiux (Shakers market analysis, n=31,957, September 2026). Operating models has a title of its own; the by-project engagement was the missing piece.
The artefact decides the profile
If what you ship is an application, DevOps; if it is a model that degrades when the data changes, MLOps. The difference is not Kubernetes or Terraform, which both master: it is versioning data and experiments, and retraining.
Connect with AI specialists to build your next project
Find specialists in Tech, Product, Data to complement your team
DevOps Engineer
The measured boundary: same Kubernetes and CI/CD stack, different artefact. If the deliverable is an application and not a model, their row takes over.
SRE
The reliability of the whole system: when the model serves but the platform is what fails, their row builds the operation that holds the traffic.
Platform Engineer
The internal platform your pipelines run on: when the team grows and self-service infrastructure is missing, their row builds it.
Data Engineer
The data channel of the model: Airflow, dbt and the pipeline that leaves the history ready. Without prepared data, your MLOps watches a flow that never arrives.
The AI talent you need Certified by Shakers
We introduce you to three ready_to_build AI Builders in 72 hours.
