Job description

Job Summary As a Principal Engineer in NetApp's AI BU, you are a senior technical authority — architecting and tech-leading complex, cross-team initiatives and setting technical direction for products at the intersection of storage, data, and AI. You are the engineer other teams turn to for the hardest design and architecture problems, and you drive outcomes through technical depth and influence rather than management authority, retaining a hands-on, individual-contributor focus. Key Responsibilities As a Principal Engineer, you will: • Serve as technical architect and/or tech lead across one or more major AI BU initiatives, owning system design end-to-end. • Set technical direction and design standards across multiple engineering teams, ensuring architectural coherence. • Drive ambiguous, cross-cutting technical problems to well-reasoned solutions, authoring design docs/RFCs that align stakeholders across teams. • Stay hands-on: write production code, review the highest stakes designs and PRs, and personally unblock the hardest engineering problems. • Partner with engineering and product leadership to translate business priorities into technical roadmaps and architecture decisions. • Mentor and raise the technical bar for engineers across teams, without formal management authority over them. • Identify systemic technical risk — architectural debt, scalability bottlenecks, reliability gaps — and drive it to resolution before it becomes a team-level fire. • Represent AI BU engineering in cross-functional and executive technical reviews, articulating trade-offs and recommendations. • Champion best practices in system design, testing, observability, and operational excellence across the teams you influence. • Evaluate and introduce new technologies, frameworks, and architectural patterns, balancing innovation with pragmatism. Key Qualifications • 12+ years of hands-on software engineering experience, including designing and building large-scale distributed systems. • Demonstrated track record as technical lead or architect on multi-team or org-wide initiatives. • Deep hands-on experience with cloud-native architectures on AWS, Azure, or Google Cloud. • Well versed in using agentic engineering using local and/or frontier models. • Hands-on experience with Distributed Storage Systems. • Proven ability to build technical consensus and influence engineering direction across teams without formal authority. • Strong system design skills, with direct ownership of architecture for highly available, low-latency, large-scale services. • Excellent written and verbal communication skills — able to author design docs/RFCs and present technical trade-offs to engineers and executives alike. • Experience with data processing, analytics, and AI/ML systems. • Track record of mentoring engineers and raising the technical quality bar across teams. • Prior experience building CI/CD infrastructure for large-scale deployments. Preferred Qualifications • Domain expertise on knowledge graphs, semantics and data modeling. • Hands-on experience with Distributed Database Systems. • Hands-on experience with training and deploying AI models. • Hands-on experience with GPU workloads and accelerated computing infrastructure. Compensation: The target salary range for this position is 196,350 - 292,600 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU’s), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process.

Requirements

Must-haves

Serve as technical architect and/or tech lead across one or more major AI BU initiatives, owning system design end-to-end.
Set technical direction and design standards across multiple engineering teams, ensuring architectural coherence.
Drive ambiguous, cross-cutting technical problems to well-reasoned solutions, authoring design docs/RFCs that align stakeholders across teams.
Stay hands-on: write production code, review the highest stakes designs and PRs, and personally unblock the hardest engineering problems.
Partner with engineering and product leadership to translate business priorities into technical roadmaps and architecture decisions.
Mentor and raise the technical bar for engineers across teams, without formal management authority over them.
Identify systemic technical risk — architectural debt, scalability bottlenecks, reliability gaps — and drive it to resolution before it becomes a team-level fire.
Represent AI BU engineering in cross-functional and executive technical reviews, articulating trade-offs and recommendations.
Champion best practices in system design, testing, observability, and operational excellence across the teams you influence.
Evaluate and introduce new technologies, frameworks, and architectural patterns, balancing innovation with pragmatism.
Key Qualifications
12+ years of hands-on software engineering experience, including designing and building large-scale distributed systems.
Demonstrated track record as technical lead or architect on multi-team or org-wide initiatives.
Deep hands-on experience with cloud-native architectures on AWS, Azure, or Google Cloud.
Well versed in using agentic engineering using local and/or frontier models.
Hands-on experience with Distributed Storage Systems.
Proven ability to build technical consensus and influence engineering direction across teams without formal authority.
Strong system design skills, with direct ownership of architecture for highly available, low-latency, large-scale services.
Excellent written and verbal communication skills — able to author design docs/RFCs and present technical trade-offs to engineers and executives alike.
Experience with data processing, analytics, and AI/ML systems.

Nice-to-haves

Domain expertise on knowledge graphs, semantics and data modeling.
Hands-on experience with Distributed Database Systems.
Hands-on experience with training and deploying AI models.
Hands-on experience with GPU workloads and accelerated computing infrastructure.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsazuremlci/cd

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Paid Time Off