Why MLOps and AIOps projects break in production.
These are the problems teams face when they scale ML without the right infrastructure expertise.
Your data science team ships models, but no one owns production deployment.
Models run in production without automated monitoring or retraining.
Inference costs spike and latency grows as usage increases.
Your IT team faces constant alert noise while real incidents get missed.
Your ML engineer can train models but cannot build reliable CI/CD pipelines.
A model silently degrades, and you discover it after business performance drops.
A great A working ML prototype and a reliable production system require different engineering skills. We place engineers who understand both.
Built for teams moving from experimental ML to reliable production systems.
If your ML or IT operations team needs specialized infrastructure expertise, this is for you.
Data Science & ML Leaders
Your models work, but production still depends on manual processes. You need automated pipelines, deployment, and monitoring.
CTOs & Platform Leaders
Your ML workloads are growing, but your infrastructure is becoming harder to manage. You need scalable ML platform engineering.
IT Operations & SRE Leaders
Your team faces alert overload and slow incident response. You need AIOps engineers who can automate detection, correlation, and remediation.
We Vet for Production Infrastructure, Not Just Tool Knowledge.
We test how engineers build, monitor, scale, and troubleshoot real ML and IT systems.
Production ML Pipelines
We test training, validation, deployment, CI/CD, and model lifecycle automation.
Monitoring, Drift Detection & Retraining
We test experience with model monitoring, data drift, concept drift, and automated retraining.
Scalable Model Serving
We test model serving, autoscaling, latency optimization, and cost control across production workloads.
AIOps & Intelligent Operations
We test event correlation, anomaly detection, incident automation, and intelligent remediation.
ML Platform Engineering
We test experience with Kubernetes, model registries, experiment tracking, feature stores, and reproducible environments.
Infrastructure Scoping
We define whether you need MLOps, AIOps, platform engineering, or a specialized infrastructure role before we match talent.
Build it yourself, hire a generalist, or match with CrecenTech.
The right infrastructure expertise can determine your system's reliability, cost, and speed to production.
From infrastructure problem to production-ready operations.
Everything you need to build AI with confidence.
They know what they are doing and can always find a solution to any complex problem that comes their way.