MLOps Engineer

Evlo AI; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Evlo AI is seeking an experienced MLOps/Platform Engineer to own production ML infrastructure, including training pipelines, model serving, monitoring, and CI/CD for experiments. You will collaborate with ML engineers and data scientists to ensure models move reliably from notebook to scalable services with cost, latency, and reproducibility as priorities.

You will build end-to-end ML pipelines, operate serving infrastructure on Kubernetes, implement CI/CD for ML, and establish monitoring for

Qualifications

• 3–6 years of experience in MLOps, ML platform engineering, or backend/DevOps engineering supporting ML systems in production.
• Strong Python and bash skills; production experience with Docker, Kubernetes, and at least one workflow orchestrator (Airflow, Kubeflow, Prefect)
• Hands-on experience deploying and monitoring models at scale on AWS, GCP, or Azure
• Deep understanding of ML lifecycle tooling: experiment tracking, model registries, lineage, and versioning (MLflow or equivalent)
• Solid infrastructure-as-code experience with Terraform or Pulumi and GitOps workflows
• BS/MS in Computer Science, Engineering, or equivalent practical experience
• Bonus: experience with GPU scheduling (Slurm, Run:ai), LLM inference optimization (vLLM, TensorRT-LLM), or building internal ML platforms used by 10+ engineers

Responsibilities

• Build and maintain end-to-end ML pipelines in Kubeflow, Airflow, or MLflow covering data ingestion, training, evaluation, and registration
• Design and operate model serving infrastructure on Kubernetes (KServe, Seldon, or Ray Serve) with autoscaling, canary deployments, and rollback strategies
• Implement CI/CD for ML systems using GitHub Actions or GitLab CI, including automated model validation, testing, and staged rollout gates
• Establish model monitoring for data drift, prediction skew, and latency/cost regression using Prometheus, Grafana, and custom alerting
• Optimize training and inference costs across GPU fleets - right-sizing instances, batching strategies, quantization where appropriate
• Manage feature store and data versioning tooling (Feast, DVC) to ensure reproducibility between training and serving environments
• Document architecture decisions, runbooks, and on-call procedures; share on-call rotation for ML platform reliability

Skills

Python

bash

Docker

Kubernetes

Airflow

Kubeflow

Prefect

Terraform

Pulumi

GitOps

Education

BS/MS in Computer Science, Engineering, or equivalent

Tools

Docker

Kubernetes

Airflow

Job description

About The Role

The role owns the infrastructure that makes machine learning work in production - training pipelines, model serving, monitoring, and the CI/CD systems that turn experiments into reliable services.

About The Role

The role owns the infrastructure that makes machine learning work in production - training pipelines, model serving, monitoring, and the CI/CD systems that turn experiments into reliable services.

You will work directly with ML engineers and data scientists to eliminate the gap between a model that works in a notebook and a model that performs reliably at scale, with cost, latency, and reproducibility as first-class requirements.

Key Responsibilities

• Build and maintain end-to-end ML pipelines in Kubeflow, Airflow, or MLflow covering data ingestion, training, evaluation, and registration
• Design and operate model serving infrastructure on Kubernetes (KServe, Seldon, or Ray Serve) with autoscaling, canary deployments, and rollback strategies
• Implement CI/CD for ML systems using GitHub Actions or GitLab CI, including automated model validation, testing, and staged rollout gates
• Establish model monitoring for data drift, prediction skew, and latency/cost regression using Prometheus, Grafana, and custom alerting
• Optimize training and inference costs across GPU fleets - right-sizing instances, batching strategies, quantization where appropriate
• Manage feature store and data versioning tooling (Feast, DVC) to ensure reproducibility between training and serving environments
• Document architecture decisions, runbooks, and on-call procedures; share on-call rotation for ML platform reliability

What We Are Looking For

• 3-6 years of experience in MLOps, ML platform engineering, or backend/DevOps engineering supporting ML systems in production
• Strong Python and bash skills; production experience with Docker, Kubernetes, and at least one workflow orchestrator (Airflow, Kubeflow, Prefect)
• Hands-on experience deploying and monitoring models at scale on AWS, GCP, or Azure
• Deep understanding of ML lifecycle tooling: experiment tracking, model registries, lineage, and versioning (MLflow or equivalent)
• Solid infrastructure-as-code experience with Terraform or Pulumi and GitOps workflows
• BS/MS in Computer Science, Engineering, or equivalent practical experience
• Bonus: experience with GPU scheduling (Slurm, Run:ai), LLM inference optimization (vLLM, TensorRT-LLM), or building internal ML platforms used by 10+ engineers