AI/ML Engineer

Job Description

The Platform Engineer – AI & GPU Services will be responsible for implementing and maintaining AI/ML platforms and GPU resource management across cloud (GCP) and on-premise infrastructure. This role combines expertise in cloud services, AI/ML technologies, and infrastructure automation to support both product engineering and platform engineering functions. The ideal candidate will have experience working with generative AI services, GPU management, and container orchestration platforms.

Responsibilities:

•  Architect, build, and maintain AI/ML platforms using Google Cloud Platform (GCP) services like Compute, Storage, IAM, and VPC.
•  Manage NVIDIA GPU resources across projects using Run.ai or similar tools.
•  Develop and maintain MLOps pipelines on platforms like Vertex AI, supporting AI/ML model training and deployment.
•  Write Python scripts for model development, automation, and infrastructure management.
•  Use Terraform for Infrastructure as Code (IaC) to automate provisioning and deployment of cloud resources.
•  Deploy and manage AI/ML models on container orchestration platforms such as OpenShift and GKE.
•  Collaborate with AI teams to facilitate LLM deployment (e.g., Llama, Mistral) and GPU utilization.
•  Automate and enhance CI/CD pipelines for seamless integration and deployment of services.
•  Monitor performance and capacity with Prometheus, Grafana, and other observability tools to ensure system stability.
•  Engage in DevOps practices, including containerization, orchestration, and infrastructure management.

Qualifications

•  Strong experience with Google Cloud Platform (GCP) and its core services (Compute, Storage, IAM, VPC).
•  Experience with GPU resource management tools (e.g., Run.ai).
•  Proficiency with Python for AI/ML workflows and automation.
•  Hands-on experience with MLOps platforms like Vertex AI.
•  Experience with Terraform for managing cloud infrastructure using Infrastructure as Code (IaC) practices.
•  Knowledge of Kubernetes and container orchestration platforms such as OpenShift and GKE.
•  Familiarity with monitoring and logging tools like Prometheus, Grafana, and the ELK Stack.
•  Proven track record of working with CI/CD pipelines and DevOps automation tools.