ML Ops Engineer

Veriipro; Town of Brookfield (WI); On-site; USD 120,000 - 160,000; Full time

Job summary

Veriipro is seeking an experienced ML Ops Engineer to design, automate, and maintain scalable ML infrastructure and deployment pipelines.

You will work with Data Scientists and DevOps to deploy models across cloud and on‑prem environments, monitor performance, and ensure governance.

The role requires 5+ years in DevOps/ML, strong Python, Docker and Kubernetes, plus hands‑on experience with CI/CD and ML platforms.

Qualifications

• 5+ years in DevOps, ML Engineering, or a related field.
• Hands-on Python scripting.
• Experience with AWS, Azure, or GCP.
• Strong knowledge of Docker and Kubernetes.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar ML platforms.
• Experience with Git, Terraform, and infrastructure automation.
• Knowledge of model monitoring, observability, data validation, and model performance tracking.
• Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures.

Responsibilities

• Build and maintain MLOps pipelines for development, deployment, monitoring, and retraining.
• Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration.
• Deploy and manage machine learning models across cloud and on-premise environments.
• Implement model versioning, experiment tracking, feature management, and model governance.
• Build scalable infrastructure using Docker, Kubernetes, and cloud services.
• Monitor model performance, data quality, system health, and production workloads.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
• Troubleshoot production ML systems and optimize reliability, scalability, and performance.
• Implement security, access controls, logging, and compliance best practices.

Skills

DevOps

ML Ops

Python

Cloud platforms

Docker

Kubernetes

CI/CD tools

MLflow Kubeflow

Terraform

REST APIs

Linux

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

GitHub Actions

GitLab CI

Jenkins

Terraform

Kubeflow

SageMaker

Vertex AI

Airflow

Job description

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience with cloud platforms, CI/CD, containerization, model deployment, monitoring, and ML lifecycle management.

Roles and Responsibilities

• Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining.
• Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration.
• Deploy and manage machine learning models across cloud and on-premise environments.
• Implement model versioning, experiment tracking, feature management, and model governance.
• Build scalable infrastructure using Docker, Kubernetes, and cloud services.
• Monitor model performance, data quality, system health, and production workloads.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
• Troubleshoot production ML systems and optimize reliability, scalability, and performance.
• Implement security, access controls, logging, and compliance best practices.

Required Skills

• 5+ years of experience in DevOps, ML Engineering, MLOps, or a related field.
• Strong experience with MLOps concepts and ML lifecycle management.
• Hands‑on experience with Python and scripting.
• Experience with AWS, Azure, or GCP.
• Strong knowledge of Docker and Kubernetes.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar ML platforms.
• Experience with Git, Terraform, and infrastructure automation.
• Knowledge of model monitoring, observability, data validation, and model performance tracking.
• Strong understanding of REST APIs, microservices, Linux, and cloud‑native architectures.

Preferred Skills

• Experience with Apache Airflow, Databricks, Spark, or Kafka.
• Knowledge of LLMOps/GenAI deployment and monitoring.
• Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton.
• Familiarity with Prometheus, Grafana, ELK, or similar observability tools.
• Understanding of ML security, governance, and responsible AI practices.

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.