Platform Engineer

This is a fully remote position, open to applicants in Canada, +1 more country.

📋 Description

• Create and establish scalable infrastructure for LLM and GenAI tasks within multi-GPU environments.

• Conduct GPU profiling, benchmarking, and performance enhancements for distributed training tasks.

• Oversee and schedule compute-heavy jobs utilizing Slurm-based clusters and OpenShift/Kubernetes setups.

• Activate and enhance the NVIDIA GPU stack.

• Collaborate with architects, data scientists, MLOps, application teams, and AI solution teams.

• Implement models in both research and production settings.

• Develop and maintain GenAI pipelines, encompassing fine-tuning, RAG, multi-modal inferencing, and LLMOps.

• Create reusable infrastructure templates using Terraform and Helm.

• Contribute to internal PoCs and workshops while supporting client-facing delivery engagements.

• Design automation software to enhance functionality, reliability, availability, and manageability of applications and cloud platforms.

• Promote the adoption of Infrastructure as Code principles.

• Design and construct self-service, self-healing, synthetic monitoring, and alerting platforms and tools.

• Automate development and testing workflows through CI/CD pipelines utilizing Git, Jenkins, SonarQube, Artifactory, and Docker.

• Develop container hosting solutions using Kubernetes.

• Introduce cloud technologies and tools to generate business value.

• Lead technical discussions with clients on architecture design and troubleshooting, proactively offering solutions.

• Guide and mentor senior resources and team leads.

⛳️ Requirements

• Over 10 years of relevant experience.

• Extensive experience with Slurm and distributed training setups.

• Practical knowledge of Red Hat OpenShift and/or Kubernetes.

• In-depth understanding of the NVIDIA GPU ecosystem: CUDA, cuDNN, NCCL, Nsight, Triton/TensorRT.

• Solid background in Linux systems, performance tuning, and multi-GPU optimization.

• Experience in deploying GenAI tasks, including LLM fine-tuning, RAG pipelines, and multi-modal systems.

• Familiarity with Infrastructure-as-Code tools like Terraform and Ansible.

• Proficient with cloud GPU environments: GCP, Azure, AWS, OCI, and/or on-premises GPU clusters.

• Experience with NVIDIA NIMs, DGX systems, or GPU-accelerated containers.

• Knowledge of LLMOps frameworks and integration with MLOps.

• Familiar with vector databases and retrieval systems for RAG architecture.

• Comfortable in client-facing roles and collaborating with AI solution teams.

• Experience in the healthcare domain is a plus, including FHIR R4, HL7 v2, SMART on FHIR, EHR integration, HIPAA, CDS Hooks, clinical workflows, and clinical decision support systems.

🏝️ Benefits

• Advance your skills and explore your potential through innovative technology projects.

• Work within a research-driven organization that has filed over 60 patents.

• Gain exposure to AI, ML, data, and cloud technologies.

• Collaborate with Fortune 500 companies.

• Enjoy opportunities for learning, growth, and global colleague interactions.

• Embrace a hybrid work culture.