MLOps Engineer

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.

• Develop and implement ML applications for prediction, recommendation, text analytics, computer vision, bots, and document intelligence.
• Utilize ML frameworks such as Tensor Flow, 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 Dev Ops 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.

• 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 Dev Ops principles.
• Deep knowledge of ML frameworks (Tensor Flow, PyTorch, Keras, Spacy, scikit‑learn).
• Ability to build and maintain scalable ML pipelines in cloud environments.