ML Engineer, Convergence AI

Responsibilities

• Design, implement, and maintain our ML-focused cloud infrastructure on GCP using Infrastructure as Code (Terraform)
• Build and manage HPC clusters with Slurm for distributed ML workloads, focusing on GPU/TPU utilization and job scheduling
• Develop and maintain ML pipeline automation tools and ML-specific CI/CD workflows in Python
• Design and optimize data storage solutions for ML datasets, model artifacts, and feature stores
• Implement comprehensive monitoring, logging, and alerting solutions for ML model performance and infrastructure health
• Collaborate with ML engineers and data scientists to provide robust infrastructure for model training and deployment
• Lead and implement security best practices for ML systems, including model security and data protection
• Requirements3+ years of experience in ML infrastructure or ML platform engineering
• Strong proficiency in Python for ML pipeline automation and tooling
• Extensive experience with Slurm cluster management for large-scale ML workloads
• Proven track record with Terraform and Infrastructure as Code for ML environments
• Solid understanding of GCP's ML-specific services (Vertex AI, AI Platform, etc.)
• Experience with distributed training systems and model serving infrastructure
• Experience with ML observability tools and performance monitoring
• Excellent problem-solving skills with a focus on ML system reliability and optimization
• Bonus QualificationsExperience scaling large language model (LLM) infrastructure
• Knowledge of ML-specific orchestration tools (e.g., MLflow, Ray)
• Experience with high-performance computing for ML training
• Contributions to ML infrastructure-related open-source projects
• Experience with GPU/TPU cluster management and optimization
• Background in ML operations (MLOps) or AI reliability engineering
• Familiarity with vector databases and efficient embedding storage/retrieval