001
Model deployment & serving
Deploy models behind stable services with reproducible builds, versioning, and runtime visibility.
Role overview
What MLOps Engineer looks like at Eventum
At Eventum, MLOps Engineers are expected to own both the infrastructure and operational layer of machine learning. They make it possible to train, deploy, monitor, scale, and maintain your models and workflows reliably in production.
When your team has models or LLM workflows that work in development, but need better deployment discipline, monitoring, observability, reliability, or cost control to support real usage, our MLOps experts deliver in spades.
Typical use cases
Typical use cases002
Evaluation & monitoring stack
Set up pipelines that track model health, drift, regressions, latency, and other operational signals.
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GPU / Kubernetes infrastructure
Support LLM or ML workloads that depend on scalable infra, orchestration, autoscaling, and cost-aware serving.
004
Training pipelines & CI/CD for ML
Build reliable workflows for retraining, testing, rollout, rollback, and infrastructure changes.
Key skills
Model deployment, serving, and monitoring
Kubernetes and containerized ML infrastructure
CI/CD for ML systems
Evaluation, observability, and alerting
Versioning and reproducibility
GPU workload management and cost optimization
Data / model pipeline orchestration
Reliability engineering for ML and LLM workloads
Testimonials
Trusted by teams who ship AI to production.
Founders, CTOs and product leads on what changed after Eventum matched them with the right AI specialist.
“Eventum matched us with an LLM engineer in six days. Two sprints later our support copilot was live in production.”
Maya LindqvistVP of Engineering, Nordflow“The vetting is real. The first candidate we interviewed was the one we hired — and she rebuilt our RAG pipeline in a month.”
Daniel WeberCTO, Parcelbase“We didn't need a whole team, we needed one senior ML engineer who could own the problem end to end. That's exactly who we got.”
Sofia MarchettiHead of Product, Klarvo“Embedded talent that actually feels in-house. Daily standups, our tooling, our codebase — zero agency friction.”
James WhitfieldFounder & CEO, Cadenza Health“Their engineer cut our inference costs by 40% in the first six weeks. The engagement paid for itself before it ended.”
Clara JensenDirector of Data, Loopwise“From brief to signed contract in under two weeks. The shortlist had three candidates, and honestly all three were hireable.”
Marcus ReinholtChief Product Officer, Ferrostack“Our computer-vision backlog was stuck for months. Eventum's specialist unblocked it in the first sprint and mentored the team along the way.”
Viktor HaldenEngineering Manager, Brightlane
Why hire through Eventum
Why hire through Eventum
01
Real infra experience, not buzzwords
We screen for engineers who have actually operated model-serving systems, not just mentioned Kubernetes on a resume.
02
ML + infrastructure fluency
The best MLOps hires understand both the software platform and the ML lifecycle.
03
Strong fit for hard-to-hire roles
These are often the hardest roles to fill well. We optimize for technical signal and practical execution.
Daria K.
MLOps Engineer · 7 yrsBuilt end-to-end ML platforms at two YC-backed companies. Specialized in GPU autoscaling on Kubernetes, model versioning with Dagster/MLflow, and LLM inference cost optimization.
KubernetesDagsterMLflowvLLMGCP
Results
Cut LLM serving costs by 58% via optimized batching and quantization
Previously Worked at:
Ivan P.
Senior MLOps Engineer · 11 yrsLed production ML infrastructure for high-volume ranking, forecasting, and LLM workflows. Strong in monitoring, deployment automation, cloud cost control, and reliability for model-serving systems.
AWSTerraformKubernetesPrometheusRay
Results
Reduced model deployment time from days to under one hour with automated release pipelines.
Previously Worked at:
A difficult ML / infrastructure search solved quickly.
A boutique AI consultancy needed senior engineers with a rare mix of Python, Kubernetes, deployment, and data-engineering experience for a time-sensitive client engagement. Eventum refined the role beyond a standard job description, ran a targeted search, and delivered a curated shortlist of high-signal candidates. The client hired two engineers, both of whom proved to be strong fits and expanded the consultancy's capacity to deliver specialized ML and infrastructure work.
- Two successful senior hires for niche technical roles
- Strong fit across infrastructure-heavy ML work
- Speed without compromising talent quality
Related roles
Other roles often hired alongside MLOps Engineers
Related case studies
