Tech Architect - PE
Free to start · No card · 5 credits the moment you sign up
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!
Originally posted on Himalayas
Prepare your application
Use the description above to assess your fit for Quantiphi. This checklist is guidance from Resumize, rather than additional requirements from the employer. Imported listings can be shortened or change after collection, so confirm the complete description and current availability before sending an application.
Match requirements to evidence
Identify the responsibilities and skills the employer explicitly names. For each important requirement, choose one example from your work, education, training, or projects. Explain what you did, how you did it, and what changed as a result. Include a measurable outcome when you can support it. If you lack a requirement, describe related experience accurately instead of adding an unfamiliar skill to your resume.
Put the most relevant examples near the top of your resume. Keep job titles, dates, qualifications, and contact details consistent across your application materials. Use the employer’s terminology when it describes your experience accurately; a copied list of keywords does not explain your contribution. A short, specific bullet is easier to review than a paragraph that mixes several unrelated duties.
Check the working arrangement
Confirm the location, schedule, employment type, and any eligibility requirements on the employer’s site. A remote label does not establish worldwide eligibility or flexible hours. If compensation, benefits, equipment, travel, or contract duration are missing from this listing, write down your questions for the recruiter. Do not assume details from another opening at the same company apply to this role.
Review before submitting
Follow the employer’s requested file format and application instructions. Open your exported resume and check that its text is selectable, its sections read in order, and its links work. Proofread names, dates, and contact information. Share confidential work samples only when you have permission, and use an anonymized example when necessary.
Submit through a trusted employer or recruiting destination. If the original listing has disappeared, search the employer’s careers page for the title rather than assuming the vacancy remains open. Save the application date, job reference, and the version of your resume you sent. Those details help you prepare for a later conversation and avoid submitting conflicting information through several job boards.
Review your resume for writing and readability feedback, or browse other openings if this opportunity is unavailable.