AI Ops Engineer

Role Overview

As an AI Ops Engineer, you will play a critical role in the design, development, and deployment of an AI platform. You will work collaboratively with cross-functional teams, focusing on MLOps & LLMops pipelines, machine learning standardization, GPU and cloud platform setup, and data integrations to ensure scalability, reliability, and efficiency.

Responsibilities

• Designing and delivering a hybrid AI Platform.
• Maintaining scalable, efficient, and reliable data and AI pipelines.
• Maintaining MLOps and LLMOps pipelines across Databricks and on-prem clusters.
• Developing and implementing AI and ML models to automate routine tasks.
• Monitoring system performance and implementing a monitoring strategy across central observability.
• Ensuring AI platform delivery meets security, risk, and operational SLA requirements.
• Collaborating with data scientists, engineers, and governance teams to align with data strategies.
• Integrating AIOps solutions with existing services like ServiceNow and ELK.
• Performing code reviews to minimize technical debt.

Requirements

• 4-6 years of experience in engineering or operation disciplines of AI Ops and ML Ops.
• Hands-on experience with GPU computing and optimization for AI workloads.
• Proven track record of delivering complex AI projects.
• Strong proficiency in Databricks, CI/CD (e.g., Azure DevOps), and machine learning frameworks.
• Experience with MLOps tools such as MLflow or Kubeflow.
• Knowledge of cloud platforms like Azure or AWS and infrastructure as code using Terraform.
• Engineering Degree or equivalent.

Skills

• Python
• Databricks
• MLOps
• Terraform
• PyTorch