Quantiphi

Platform Engineer

• Orchestrating LLM Workflows & Development: Design, implement, and scale the underlying platform that supports GenAI workloads, be it for real-time or batch. The workloads can also vary from fine-tuning/distilling to inference.
• LLMOps (LLM Operations): Build and manage operational pipelines for training, fine-tuning, and deploying LLMs such as Llama, Mistral etc, GPT-3/4, BERT, or similar. Ensure smooth integration of these models into production systems.
• GPU Optimization: Optimize GPU utilization and resource management for AI workloads, ensuring efficient scaling, low latency, and high throughput in model training and inference. Develop techniques to manage multi-GPU systems for high-performance computation. Have clarity on LLM parallelization techniques as well as other inference optimization techniques.
• Infrastructure Design & Automation: Design, deploy, and automate scalable, secure, and cost-effective infrastructure for training and running AI models. Work with cloud providers (AWS, GCP, Azure) to provision the necessary resources, implement auto-scaling, and manage distributed training environments.
• Platform Reliability & Monitoring: Implement robust monitoring systems to track the performance, health, and efficiency of deployed AI models and workflows. Troubleshoot issues in real-time and optimize system performance for seamless operations. Transferable knowledge from traditional software monitoring in production is fine. Monitoring knowledge of ML/GenAI workloads is preferred.
• Maintain Knowledge Base: Good knowledge of database concepts ranging from performance tuning, RBAC, shading, along with exposure to different types of databases from relational to object & vector databases is preferred.
• Collaboration with AI/ML Teams: Work closely with data scientists, machine learning engineers, and product teams to understand and support their platform requirements, ensuring the infrastructure is capable of meeting the needs of AI model deployment and experimentation.
• Security & Compliance: Ensure that platform infrastructure is secure, compliant with organizational policies, and follows best practices for managing sensitive data and AI model deployment.

Senior Platform Engineer