Machine Learning Engineer

Position 1 : MLOps Engineer II (Mid-Level)
Location : Remote (Night Shift – 10 PM to 7 AM CST)
Contract : 12 months (Renewable)
Start Date : December 1, 2025
Role Overview
We are seeking a proficient MLOps Engineer (Level II) with 2–4 years of experience in ML operations, pipelines, and production deployments .

The ideal candidate will be hands-on, detail-oriented, and capable of working independently to ensure ML models are deployed, monitored, and maintained effectively.
Responsibilities
Deploy and maintain ML models in production (AWS SageMaker, MLflow, Kubeflow).
Manage CI/CD pipelines and infrastructure as code (Terraform, ArgoCD).
Support ETL/data workflows using Airflow or Astronomer.
Collaborate with data scientists to transition models into production environments.
Troubleshoot issues, perform root cause analysis, and drive remediation.
Qualifications
2–4 years of experience in MLOps or DevOps .
Strong Python skills for automation & workflow management.
Hands-on with cloud ML tools and orchestration frameworks.
Familiarity with CI/CD and monitoring best practices.
Position 2 : MLOps Engineer III (Senior-Level)
Location : Remote (Night Shift – 10 PM to 7 AM CST)
Contract : 12 months (renewable)
Start Date : December 1, 2025
Role Overview
We are hiring a Senior MLOps Engineer (Level III) with 4–6 years of experience designing and scaling ML infrastructure .

This role requires technical leadership, mentorship, and deep expertise in automation and hybrid environments.

The Engineer III will set best practices, lead monitoring strategies, and ensure reliable integration across teams.
Responsibilities
Lead design of scalable MLOps frameworks and automation strategies.
Build and optimize monitoring/alerting systems for drift, accuracy, and latency .
Manage governance and versioning with GitHub Enterprise and JFrog Artifactory.
Enhance deployment/retraining pipelines (Terraform, ArgoCD).
Partner with Big Data and Work Routing teams for system reliability.
Mentor junior engineers and lead knowledge transfer sessions .
Qualifications
4–6 years of experience in MLOps, DevOps, or ML Engineering .
Advanced proficiency in automation, pipelines, and hybrid infra (on-prem + cloud) .
Hands-on expertise with Terraform, ArgoCD, and orchestration tools.
Strong leadership and communication skills.
Why Join?
Be part of an INSPYR Solutions Managed Service team .
Work on cutting-edge production ML infrastructure .
Fast selection : one interview with the hiring manager.
Opportunity to contribute to a long-term, high-impact engagement .