Building a model is half the job. Keeping it running reliably under real traffic, drifting data and inevitable failures is the other half — and it's usually the half no one staffed for. For 10+ years we've placed engineers with 70+ businesses, including MLOps and AIOps specialists who make models production-grade.
These are the operational gaps an MLOps engineer closes.
Deploying a model still means someone manually babysitting the process.
No one notices when model accuracy quietly degrades over time.
Your data scientists are stuck doing DevOps work they weren't hired for.
Rolling back a bad model update is slow and stressful when it happens.
Infrastructure costs balloon because inference isn't optimized.
Retraining is a manual, dreaded process instead of a routine pipeline.
A great model with no operational discipline behind it is a liability. We place engineers who build the pipelines, monitoring and guardrails that keep models trustworthy long after launch.
If your ML is more than an experiment now, this is for you.
Your data science team has a working model that never made it to production.
You're serving several models now and manual processes don't scale anymore.
Your team is stuck doing infrastructure work instead of building models.
Each problem above maps to how we staff. Here's how we fix it.
CI/CD for models, so deployment is a routine step, not a manual event.
Alerts when model performance degrades, before it hurts real outcomes.
We own the infrastructure so your data scientists get back to modeling.
Versioned models and one-step rollback, so a bad update is a non-event.
Right-sized infrastructure so you're not overpaying to serve predictions.
Automated retraining pipelines keep models current without manual effort.
We operate ML systems for our own products, so our engineers understand the operational side, not just the modeling side. For 10+ years and 70+ clients, we place people who've kept real models running under real load — not just trained one once.
Book a Talent Call →The same model, three very different production outcomes.
We scope your models, stack and scale — no cost, no obligation.
We shortlist an MLOps engineer fit for your infrastructure.
They set up deployment, monitoring and retraining as one system.
Models run reliably, and we scale the operation as you add more.
They know what they are doing and can always find a solution to any complex problem that comes their way.
A data scientist builds the model. An MLOps engineer makes it run reliably in production — deployment pipelines, monitoring, retraining, rollback — the operational half most teams underestimate.
Notebooks don't handle real traffic, drifting data or failures gracefully. MLOps engineers build the infrastructure that keeps a model performing once it's live, not just when you tested it once.
Yes. Our engineers work across the common cloud platforms and ML tooling, integrating with what you already have rather than requiring a rebuild.
We match from an existing vetted pool, so you're typically interviewing within days and can have someone contributing shortly after.
Both. Many clients start on contract to prove fit on real work, then convert strong performers to permanent.
Book a talent call — we'll scope your ML infrastructure and match an engineer who keeps models reliable.