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Model deployment & serving
Deploy models behind stable services with reproducible builds, versioning, and runtime visibility.
Role overview
What an 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.
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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 helped us go from stuck to cutting edge in a matter of months, rewriting our entire ML training stack and continually supporting our R&D efforts.”
Kevin JacobsVP Data Science, Deepcell“Eventum came in and quenched our MLOps fire in quick order ensuring our ML Scientists could make rapid progress. We then immediately hired them to help build us a generative AI audio model from scratch”
Shawn ZhangCTO / Founder, Sanas"Eventum built and managed our ML team, models, and software from the ground up at below market rates delivering incredible results. They are an essential partner for us that I couldn’t recommend more highly."
Jim BenedettoCAO, PLAI Labs
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:
ML infrastructure that keeps pace with the models.
Sanas was shipping models faster than its infrastructure could handle. Training runs were expensive, deployments were manual, and the data pipeline had become the bottleneck for every release. Eventum embedded senior MLOps and data engineering directly into the team, rebuilding the ETL layer and automating the path from training to production. The result was a release cycle the team could actually rely on, at a fraction of the previous infrastructure cost.
- 40% faster ML dev cycle
- ~70% lower infrastructure cost
- Automated release pipeline from training to production
Related roles
Other roles often hired alongside MLOps Engineers
Related case studies
