Machine Learning Ops Engineer

We are seeking a  MLOps Engineer to design, implement, and manage scalable machine learning infrastructure and automation pipelines. The ideal candidate will have deep hands-on expertise in Azure, AKS, Infrastructure as Code, and CI/CD, with a passion for enabling efficient and reliable deployment of machine learning models in production environments.

Responsibilities:-

• Architect & Deploy:

  Design and manage scalable ML infrastructure on Azure (AKS), leveraging Infrastructure as Code principles.

• Automate & Accelerate:

  Build and optimize CI/CD pipelines with GitHub Actions for seamless software, data, and model delivery.

• Engineer Performance:

  Develop efficient and reliable data pipelines using Python and distributed computing frameworks.

• Ensure Reliability

  : Implement solutions for deploying and maintaining ML models in production.

• Collaborate & Innovate

  : Partner with data scientists and engineers to continuously enhance existing MLOps capabilities.

Expertise:-

• Azure & AKS

  : Deep hands-on experience.

• IaC & CI/CD

  : Mastery of Terraform/Bicep & GitHub Actions.

• Data Engineering

  : Advanced Python & Spark for complex pipelines.

• ML Operations

  : Proven ability in model serving & monitoring.

• Problem Solver:

  Adept at navigating complex technical challenges and delivering solutions.

Nice to Have:-

• Experience with Kubeflow, MLflow, or similar MLOps tools.
• Exposure to other cloud platforms (AWS, GCP) in addition to Azure.
• Familiarity with security, compliance, and cost-optimization for ML workloads.