Mateo
Full Stack Developer
ReactNode.jsPythonTypeScriptNext.jsMongoDBAWSAIAPIs
Shakers for companies
Work with an AI Reliability Engineer
A certified AI Reliability Engineer for when the pilot that worked starts to degrade: continuous model evaluation, regression and drift monitoring, and a clear answer to who responds the day the AI gets it wrong.
Senior tech talent for your AI in production, starting in days on the systems you already have, without growing headcount.
- 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, AI Reliability Engineer 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
AI Reliability Engineer: what this role brings to your project
An AI Reliability Engineer sustains AI in production: continuous model evaluation (evals with real cases and LLM-as-judge where it helps), regression watch between versions, drift detection when the world changes, and the guardrails and traces that answer why it failed.
They are the SRE of AI: the discipline that keeps classic systems running, applied to models that degrade silently. The AI Engineer builds; this role guarantees what's built still works on Thursday.
evalsLLM-as-judgeregressiondriftmonitoringguardrailsMLflowDeepEvaltracesSLAPythoncost per query
One provider. One contract. Full traceability
How Shakers works for your company
Post your project
Tell us about the project, its scope, the expertise you need and when you want to get to work.
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
Degradation gets seen coming
Monitoring on metrics your business cares about: the answer that degrades is caught on Tuesday, not when the customer calls.
Regression measured between versions
Every model or prompt change passes the evaluation battery: the improvement that breaks other cases never reaches production.
Drift detected in time
When the world changes relative to the data the model was born on, the panel flags it before the answers give it away.
Guardrails with an owner
What the AI can do alone, what escalates to a human and what gets blocked: limits written and tested, not promised in a slide.
Traces that answer why
Every important answer leaves a trail: what context it used, what decision it made and which model version responded.
Cost per query under control
The inference bill measured per use case: model routing gets decided with the cost on the table.
Frequently asked questions
Any other questions? Our team will answer them.
Tell us your data projectWhat are evaluations of an AI model?
Real cases from your business that every model version is measured against, with explicit success criteria and LLM-as-judge where it helps. The difference between believing it works and knowing it.
Isn't AI Reliability Engineer the same as AI Engineer?
The AI Engineer builds and integrates the model; the reliability engineer guarantees it keeps working: evals, regression, drift and guardrails. Like devops and SRE: building and sustaining.
How do you measure if a model fits my case?
With your own cases: a battery is built with real business examples, what's tolerated and what isn't gets fixed, and every model change passes through it before shipping.
Which evaluation tools are used?
MLflow, DeepEval or whatever your stack needs: the tool is learned in a week. What's decided with you is what to evaluate, how often and who answers when the metric moves.
Can AI reliability work be done remotely?
Almost all: panels and evaluations run in the cloud and sessions with the team happen remotely. Only the occasional kickoff asks for presence.
What does a reliability engineer need to start?
Access to the model in production, the cases where it already fails and one session to fix what gets measured first. With that, the first battery arrives in days.
What gets delivered in the first weeks
An evaluation battery over real cases from your business, the panel watching regression and drift, and the guardrail protocol: what's measured, how often, and who answers when the metric moves.
The category exists; the role doesn't
The SERP for llm evaluation is owned by tools: Arize, MLflow, DeepEval. 111 live UK tech vacancies already mention llm evaluation, according to Shakers' market analysis (n=39,811, September 2026). Nobody answers who watches a company's model: this page creates that place.
The symptom that gives it away
The pilot charmed, the demo passed, and three weeks later the answers degraded: the model, the world or the data changed, and nobody measured it.
Tools aren't the trade
MLflow or DeepEval are learned in a week; the judgement of what to evaluate and what to tolerate isn't. The brief brings the tools your stack needs.
Connect with AI specialists to build your next project
Find specialists in Tech, Product, Data to complement your team
AI Engineer
Built what this role sustains: the model integration, API and product layer that reliability watches over.
AI Agent Developer
The agent's guardrails are designed with whoever built it: the frontier between orchestrating and sustaining gets agreed per project.
AWS DevOps Engineer
The classic operations this trade was born from: infrastructure, deployments and the SRE that has always existed in your stack.
Backend Developer
The systems the AI touches: when traceability needs hooks in the product, that's the role that puts them.
The AI talent you need Certified by Shakers
We introduce you to three ready_to_build AI Builders in 72 hours.
