MLOps Engineer

Compunnel, Inc.; Plano (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

A tech solutions provider is seeking an experienced MLOps Engineer in Plano, Texas to design, deploy, and maintain machine learning solutions in cloud environments. The role emphasizes building scalable ML pipelines using Azure ML and Kubernetes, managing model validation and deployment, and applying modern MLOps practices. Ideal candidates will have over 8 years of experience in MLOps and strong skills in Python and relevant ML frameworks. A competitive package is offered along with growth opportunities.

Qualifications

• Minimum 8+ years of experience in MLOps and ML application development.
• Expertise in Azure ML Studio and Azure Kubernetes Service.
• Hands‑on experience with Argo or Bento for ML workflow orchestration.

Responsibilities

• Develop and implement ML applications for various tasks.
• Utilize ML frameworks for model development.
• Work with Azure ML Studio and Azure Kubernetes Service.

Skills

Azure ML services

Kubernetes

Python

ML frameworks (TensorFlow, PyTorch, Keras)

DevOps principles

Automation frameworks

Tools

Terraform

Azure Kubernetes Service

Azure ML Studio

Job description

We are seeking an experienced MLOps Engineer to design, deploy, and maintain machine learning solutions in cloud environments.

This role focuses on building scalable ML pipelines, managing model inferencing and validation, and ensuring robust deployments using modern MLOps practices.

The ideal candidate will have deep expertise in Azure ML services, Kubernetes, and automation frameworks.

Key Responsibilities

• Develop and implement ML applications for prediction, recommendation, text analytics, computer vision, bots, and document intelligence.
• Utilize ML frameworks such as TensorFlow, PyTorch, Keras, Spacy, and scikit-learn for model development.
• Work with Azure ML Studio and Azure Kubernetes Service for model deployment and orchestration.
• Deploy Azure cloud services using Terraform templates and apply DevOps principles for automated deployments.
• Leverage Python frameworks such as Django, Flask, Django Rest, or FastAPI for application development.
• Manage model inferencing, validation, and deployment to ensure quality and compliance.
• Create and maintain infrastructure for ingesting, normalizing, and combining datasets for actionable insights.
• Communicate complex technical concepts clearly to non-technical audiences and ensure client satisfaction.
• Collaborate with stakeholders to ensure successful project delivery.

Required Qualifications

• Minimum 8+ years of experience in MLOps and ML application development.
• Expertise in Azure ML Studio, Azure Kubernetes Service, and model inferencing.
• Hands‑on experience with Argo or Bento for ML workflow orchestration.
• Strong proficiency in Python and related open‑source frameworks.
• Experience deploying Azure cloud services using Terraform and applying DevOps principles.
• Deep knowledge of ML frameworks (TensorFlow, PyTorch, Keras, Spacy, scikit‑learn).
• Ability to build and maintain scalable ML pipelines in cloud environments.