MLOps Engineer

Evlo AI; Seattle (WA); On-site; USD 140,000 - 190,000; Full time

Job summary

Evlo AI is seeking an experienced MLOps Engineer to build and operate the infrastructure that moves ML models from experimentation to reliable production systems. You will own training and inference pipelines, registries, feature workflows, deployment automation, and observability across cloud environments.

Working with ML engineers, data scientists, and platform engineers, you will improve speed and safety of model releases while maintaining latency, scalability, security, and reproducibility

Qualifications

• 3–8 years of experience in MLOps, machine learning engineering, platform engineering, DevOps, or related discipline.
• Strong Python and SQL skills, with experience building services, automation, data pipelines, and developer tooling.
• Hands‑on experience with Kubernetes, Docker, Linux, cloud infrastructure, and IaC tools such as Terraform.
• Experience deploying and operating ML models using platforms like MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or Argo.
• Solid understanding of ML lifecycle management, model versioning, data lineage, reproducibility, monitoring, and rollback practices.
• Bachelor’s degree in CS/Engineering/Math or related technical field; equivalent experience considered.
• Bonus: GPU scheduling, distributed training, Ray, Spark, Feast, LLM inference, model quantization, service meshes, or real-time serving systems

Responsibilities

• Build and maintain automated CI/CD pipelines for model training, validation, packaging, and deployment.
• Design reproducible ML workflows with Kubeflow, Airflow, Argo, MLflow, or equivalent platforms.
• Deploy and scale online and batch inference services across AWS, GCP, or Azure.
• Implement model and data observability for drift, data quality, latency, and resource use using Prometheus/Grafana.
• Manage model registries, feature stores, artifacts, and IaC with Terraform/CloudFormation.
• Partner with ML and data science teams to standardize training environments and release processes.
• Strengthen production ops with automated testing, incident response, security controls, and SLAs.

Skills

Python

SQL

Kubernetes

Docker

Linux

Cloud infra

IaC (Terraform)

CI/CD

DevOps tooling

ML tooling

Education

Bachelor’s degree in CS/Engineering/Math or related field

Tools

Kubeflow

Airflow

Argo Workflows

MLflow

SageMaker

Vertex AI

Terraform

Job description

About The Role

The MLOps Engineer will build and operate the infrastructure that moves machine learning models from experimentation into reliable production systems. The role covers training and inference pipelines, model registries, feature and data workflows, deployment automation, and observability across cloud environments.

Working closely with ML engineers, data scientists, and platform engineers, this role will improve the speed and safety of model releases while maintaining production standards for latency, scalability, security, and reproducibility. The work will support real-time and batch workloads, including deep learning and LLM-based applications.

Key Responsibilities

• Build and maintain automated CI/CD pipelines for model training, validation, packaging, and deployment using Python, Docker, Kubernetes, and GitHub Actions or GitLab CI
• Design reproducible ML workflows with tools such as Kubeflow, Airflow, Argo Workflows, MLflow, or equivalent platforms
• Deploy and scale online and batch inference services across AWS, GCP, or Azure, optimizing compute utilization, latency, and reliability
• Implement model and data observability for drift, data quality, prediction performance, latency, error rates, and resource consumption using Prometheus, Grafana, or comparable tooling
• Manage model registries, feature stores, artifact repositories, and infrastructure-as-code with Terraform or CloudFormation
• Partner with ML and data science teams to standardize training environments, release processes, experiment tracking, and rollback procedures
• Strengthen production operations through automated testing, incident response, security controls, documentation, and service-level objectives

What We Are Looking For

• 3–8 years of experience in MLOps, machine learning engineering, platform engineering, DevOps, or a closely related discipline, including experience supporting production ML systems
• Strong Python and SQL skills, with practical experience building services, automation, data pipelines, and developer tooling
• Hands‑on experience with Kubernetes, Docker, Linux, cloud infrastructure, and infrastructure-as-code tools such as Terraform
• Experience deploying and operating ML models using platforms or tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, Airflow, or Argo
• Solid understanding of ML lifecycle management, model versioning, feature and training data lineage, reproducibility, monitoring, and responsible rollback practices
• Bachelor’s degree in computer science, engineering, mathematics, statistics, or a related technical field; equivalent professional experience is also considered
• Bonus: experience with GPU scheduling, distributed training, Ray, Spark, Feast, LLM inference, model quantization, service meshes, or high-throughput real-time serving systems