Architect - Platform Engineer

Quantiphi

2d•Remote

About The Position

We are looking for a highly skilled Architect - Platform Engineer to design, optimize, and scale infrastructure for GenAI and LLM workloads. This role is ideal for someone with deep hands-on experience in GPU profiling, distributed training, and high-performance compute environments. You will be working with Architects from other specialties such as Data engineering, Software engineering, ML engineering to create platforms, solutions and applications that cater to latest trends. You’ll play a key role in building out GenAI platform foundations, supporting production-grade deployments, and partnering closely with data science, MLOps, and application teams to bring cutting-edge AI solutions to life.

Requirements

Strong experience with Slurm and distributed training environments
Hands-on expertise with Red Hat OpenShift and/or Kubernetes
Deep knowledge of the NVIDIA GPU ecosystem (CUDA, cuDNN, NCCL, Nsight, Triton/TensorRT)
Strong foundation in Linux systems, performance tuning, and multi-GPU optimization
Experience deploying GenAI workloads (LLM fine-tuning, RAG pipelines, multi-modal systems)
Familiarity with Infrastructure-as-Code tools (Terraform, Ansible)
Experience with cloud GPU environments (GCP, Azure, AWS, OCI) and/or on-prem GPU clusters
Serve as a mentor or guide for senior resources / team leads.
Lead the technical discussion regarding architecture design

Nice To Haves

Experience with NVIDIA NIMs, DGX systems, or GPU-accelerated containers
Knowledge of LLMOps frameworks and MLOps integration
Familiarity with vector databases and retrieval systems for RAG architectures
Comfortable working in client-facing environments and collaborating with AI solution teams
Healthcare Domain Experience
Experience working with FHIR R4, HL7 v2, or SMART on FHIR
Integration with EHR systems (e.g., Epic)
Understanding of HIPAA compliance and healthcare data privacy
Exposure to clinical workflows, CDS Hooks, or patient-facing applications
Experience building clinical decision support systems or healthcare interoperability solutions

Responsibilities

Design and implement scalable infrastructure for LLM and GenAI workloads across multi-GPU environments
Perform GPU profiling, benchmarking, and performance optimization for distributed training workloads
Manage and schedule compute-intensive jobs using Slurm-based clusters and OpenShift/Kubernetes environments
Enable and optimize the NVIDIA GPU stack (CUDA, cuDNN, NCCL, Triton, RAPIDS, etc.)
Collaborate with cross-functional teams to deploy models in research and production environments
Build and support GenAI pipelines (fine-tuning, RAG, multi-modal inferencing, LLMOps)
Develop reusable infrastructure templates using tools like Terraform and Helm
Contribute to internal innovation (PoCs, workshops) and support client-facing delivery engagements
Develop and deliver automation software required for building & improving the functionality, reliability, availability, and manageability of applications and cloud platforms
Champion and drive the adoption of Infrastructure as Code (IaC) practices and mindset
Design, architect, and build self-service, self-healing, synthetic monitoring and alerting platform and tools
Automate the development and test automation processes through CI/CD pipeline (Git, Jenkins, SonarQube, Artifactory, Docker containers)
Build container hosting-platform using Kubernetes
Introduce new cloud technologies, tools; processes to keep innovating in the commerce area to drive greater business value.
Lead the technical discussion regarding architecture designing and troubleshooting with the clients and provide solutions proactively as required