MLOps Engineer

Nouvells; United States; Remote; USD 150,000 - 260,000; Full time

Job summary

Nouvells is seeking a founding MLOps Engineer to own the end-to-end operational lifecycle of our ML systems. You will turn exploratory notebooks into resilient, containerized microservices and lead deployment, monitoring, and scaling across distributed cloud environments.

You will collaborate with data scientists and platform engineers to define model validation, versioning, serving, and observability, shaping an early-stage ML platform in a greenfield setting.

Qualifications

• Bachelor's degree in CS or related field required.
• Experience turning notebooks/models into production services.
• Familiarity with cloud environments and CI/CD for ML.

Responsibilities

• Design and deploy low-latency model inference engines using frameworks like Triton, vLLM, TensorRT, or TGI.
• Architect and manage scalable GPU/CPU clusters with Kubernetes (EKS/GKE) or Ray.
• Build feature ingestion, caching, and model registry pipelines with MLflow, Feast, or Weights & Biases.
• Collaborate with Ops to establish incident response runbooks and automated alerts.

Skills

MLOps

Model deployment

Cloud orchestration

Monitoring

Education

B.Sc in Computer Science or related field

Tools

Kubernetes

MLflow

TensorRT

Weights & Biases

Job description

Nouvells is looking for MLOps Engineers who can build the infrastructure and operational processes required to reliably deploy, monitor, and maintain machine learning systems.You'll bridge machine learning development and production engineering.

As our Founding MLOps Engineer, you will take full ownership of the end-to-end operational lifecycle of Nouvell's machine learning systems. You will step into a greenfield environment, turning raw exploratory notebooks and prototype models into resilient, containerized, and auto-scaling microservices.You will partner closely with data scientists, backend platform engineers, and operations leadership to define how models are validated, deployed, versioned, served, and monitored across distributed cloud environments.

Key responsibilities

• Design and deploy low-latency, high-concurrency model inference engines utilizing serving frameworks such as Triton Inference Server, vLLM, TensorRT, or TGI.
• Architect and manage scalable GPU/CPU clusters using Kubernetes (EKS/GKE), Ray, or specialized bare-metal GPU cloud providers.
• Build robust feature ingestion, caching, and model registry pipelines using tools like MLflow, Feast, or Weights & Biases.
• Collaborate with Operations to establish incident response runbooks, automated alerts, and cost-allocation dashboards by model workload.

Qualifications

• B.Sc in computer Science or any related field