Tech Architect at Quantiphi

Job description

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi! About Quantiphi: Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients. Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries. Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters. We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including: 21 Google Cloud Partner of the Year awards in the past 10 years 3 AWS AI/ML Partner of the Year awards 3 NVIDIA Partner of the Year awards 3 Snowflake Partner of the Year awards Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators. We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture. For more details, visit: Website or LinkedIn Page Role: Architect - Platform Engineering Experience Level: 10 years Employment type : Full Time Location: Remote (Canada/US) Role Overview: 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’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. Key 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. Basic Qualification (BQ): 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. Other Qualifications (OQs): 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 (Nice to Have): 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. What’s in it for YOU at Quantiphi: Make an impact at one of the world’s fastest-growing AI-first digital engineering companies. Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues. Work where innovation happens - work with disruptive innovators in a research-focused organization with 60 patents filed across various disciplines. Stay ahead of the curve, immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies. If you like wild growth and working with happy, enthusiastic over-achievers, you ll enjoy your career with us !

Requirements

Must-haves

If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
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.
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.
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.
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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

aigoawsmlllmkubernetesgcpazure

Platform: workday
Effort: long