MLOps Engineer

Role Overview

MLOps Engineer (Temp W2 or C2C) for a 100% remote engagement. You will design and deploy ML applications end-to-end, focusing on inference, validation, and production-grade MLOps on Azure.

Responsibilities

• Ideate and develop ML applications across areas such as:
  • prediction, recommendation
  • text analytics, computer vision
  • bots and document intelligence
• Apply deep ML framework expertise (TensorFlow, PyTorch, Keras, spaCy, scikit-learn).
• Use Azure ML Studio and Azure Kubernetes Service to operationalize ML workloads.
• Deploy Azure services using Terraform and follow DevOps principles for automated deployments.
• Build and maintain ML infrastructure to ingest, normalize, and combine datasets for actionable insights.
• Perform model inferencing, validation, and deployment to ensure appropriate quality and validation.
• Use Python web frameworks/APIs such as Django/Flask and Django REST Framework/FastAPI.
• Communicate complex technical concepts clearly to non-technical audiences.
• Collaborate with clients to support satisfaction and project success.

Requirements

• 8+ years of experience in:
  • MLOps
  • Azure
  • Azure Kubernetes
  • Argo / Bento (as referenced)
  • Azure ML Studio
  • Model inferencing

Nice-to-haves

• Additional strength in dataset engineering for ingestion/normalization pipelines.
• Strong DevOps automation practices integrated with infrastructure provisioning (Terraform).