Principal AI Infra Architect - Remote, High-Performance

EPAM Systems Inc; McLean (VA); Remote; USD 180,000 - 220,000; Full time

Benefits offered by this job

Medical, Dental and Vision Insurance

401(k) retirement match

LinkedIn learning access

Paid time off and holidays

Job summary

EPAM Systems, Inc. is seeking a Principal Architect to drive the architectural vision for high-performance AI infrastructure, including training and serving workloads, across cutting-edge hardware platforms.

The role leads the Center of Excellence, partnering with ML researchers, framework teams, and compiler engineers to remove bottlenecks and shape future hardware/software requirements for scalable AI systems.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, or equivalent practical experience (Master's or Ph.D. preferred)
• 15+ years of software engineering experience, with 8+ years in distributed systems, AI infrastructure, or HPC architecture
• 7+ years designing and developing complex software systems in C++ or Python
• 5+ years leading architecture, design, and delivery of large-scale software products/frameworks

Responsibilities

• Define multi-year technical roadmap for high-performance AI kernels and hardware-software co-design
• Scale and mentor a world-class technical practice with governance and standards
• Act as principal liaison with ML researchers, core framework architects (JAX, PyTorch), and compiler teams (XLA, MLIR)
• Architect foundational infrastructure including benchmarking suites, autotuning, regression analysis, and docs
• Anticipate industry shifts to unlock step-changes in AI training and inference efficiency

Skills

Distributed systems

AI infrastructure

High-performance computing

C++/Python

Architectural leadership

Education

Bachelor's degree in Computer Science, Electrical Engineering, or equivalent

Master's or Ph.D. preferred

Job description

EPAM Systems, Inc. is seeking a Principal Architect to drive the architectural vision for high-performance AI infrastructure, including training and serving workloads, across cutting-edge hardware platforms.

The role leads the Center of Excellence, partnering with ML researchers, framework teams, and compiler engineers to remove bottlenecks and shape future hardware/software requirements for scalable AI systems.