Description

You will design and manage the infrastructure and deployment pipelines for machine learning workloads.

This role is on-site.

Responsibilities

• Build and maintain CI/CD pipelines for automated ML model deployment.
• Manage containerized environments using Kubernetes and Docker.
• Implement infrastructure-as-code using Terraform and Ansible.
• Orchestrate complex ML pipelines using Kubeflow, MLflow, or Airflow.
• Monitor and ensure observability for ML workloads across cloud-native architectures.

Required Skills

• 12+ years of professional experience in software or ML engineering.
• Expertise in Python, including hands-on experience with TensorFlow and PyTorch.
• Proficiency with Kubernetes and Docker.
• Experience with Terraform and Ansible.
• Experience with cloud-native architectures in GCP and Azure.
• Advanced understanding of orchestration tools such as Kubeflow, MLflow, Airflow, or TFX.
• Knowledge of monitoring and observability for ML workloads.

Preferred Skills

• Experience with distributed computing frameworks like Spark, Ray, or Dask.
• Familiarity with model explainability, fairness, and bias detection tools.
• Knowledge of security best practices including data encryption, API security, and governance.

Key Skills

Education

Any Gradute