Principal AI Developer Experience Architect

SentinelOne

31d•$216,000 - $260,000

About The Position

As a Principal AI Developer Experience Architect, you will serve as the technical authority for the AI engineering layer at SentinelOne, designing how enterprise-grade AI coding assistants and LLM platforms integrate into our engineering workflows. You will own the strategy and architecture that ensures AI-generated code is trustworthy, performant, and reliable by default for an 800+ developer organization. Your work will ensure our AI tooling investment is secure, governed, and measurably impactful across context management, prompt design, and AI-driven code review.

Requirements

10+ years in software engineering, platform/dev tools, or infrastructure engineering, including significant experience operating at Staff/Principal level in a 300+ engineer product organization.
3+ years designing and shipping LLM‑powered developer tools or workflows in production environments.
Proven track record of architecting developer platforms or large‑scale internal tooling used by hundreds of engineers.
Deep understanding of LLM context window design, retrieval strategies, prompt engineering at scale, and how they interact with large polyglot codebases.
Hands-on experience integrating at least one enterprise AI coding assistant into IDEs and Git hosting platforms.
Strong background in AST-based code analysis, static/dynamic analysis, and policy-as-code for automated PR review.
Solid understanding of CI/CD systems and progressive delivery and how to embed AI in those pipelines.
Strong grounding in security and compliance for SaaS and AI/ML systems
Experience acting as a cross‑org technical leader: aligning Staff/Principal engineers, EMs, and PMs around a shared architecture and standard.
Comfortable presenting technical tradeoffs and ROI to senior leadership, and defending architecture choices with data.
Proven ability to mentor and partner with the entire Product and Technology organization to deliver complex multi‑quarter programs.
Excellent written and verbal communication skills; able to produce clear design docs, evaluation criteria, and developer‑facing guidance that drive alignment at scale.

Responsibilities

Architect the AI Engineering Layer: Define the end‑to‑end architecture for AI-augmented development at SentinelOne, centered on enterprise-grade AI coding assistants and LLM platforms integrated with our IDEs, CI/CD pipelines, and internal platforms.
Design how large-context LLMs consume our codebase and documentation context so that AI assistance scales safely across hundreds of repos.
Establish reusable patterns for agentic workflows with clear rollback and validation strategies.
Own AI Guardrails, Skills, and Standards: Define and maintain SentinelOne AI Coding Standards and a global skills & requirements framework that all AI coding tools (our chosen AI coding assistants, PR bots, agents) must enforce, including language, framework, security, performance, and reliability patterns.
Translate these standards into prompt templates, tool configurations, and policy‑as‑code so they are enforced automatically rather than via documentation alone.
Partner with InfoSec, Data Governance, and Legal to ensure all AI tooling adheres to SentinelOne’s AI Policies, data residency, and FedRAMP compliance requirements.
Implement AI-Driven Code Review and Safety as Code: Lead the integration of AI-powered code review tools that use AST analysis and static/dynamic checks to enforce style, security, performance, and reliability standards before human review.
Integrate AI review signals with our SRE observability stack (logs, metrics, traces) to correlate code changes with incidents and anomalies, closing the loop from PR to production behavior.
Encode “safety as code” by integrating test coverage, performance scans, and progressive delivery patterns (canaries, blue/green) into AI‑authored change flows.
Lead Vendor Evaluation and Integration: Serve as the principal technical owner for AI developer tooling evaluations using structured evaluation criteria and data-driven analysis.
Work closely with the DevEx leadership and TPM to design and run POCs, define technical success criteria, and produce executive‑ready recommendations.
Ensure all vendors meet SentinelOne’s security, privacy, and data governance standards.
Drive Developer Adoption and Experience: Act as the technical face of AI DevEx to engineering: publish guidance, patterns, and internal frameworks that make AI tooling a seamless part of everyday work rather than a side experiment.
Partner with the AI Champions Network and Learning & Development to define curricula, hands-on labs, and “day‑in‑the‑life” workflows that level up prompt engineering and AI‑assisted development skills across developer organizations.
Collaborate across the SentinelOne developer community to translate adoption and productivity signals into DORA metrics and ROI narratives that are credible to Engineering Leadership, Finance, and the CFO.
Partner on Reliability, Metrics, and Governance: Work with SRE and Reliability Engineering to ensure AI‑accelerated delivery improves MTTR, CFR, and SLO performance.
Participate in the DevEx governance model to prioritize initiatives, manage risk, and align AI DevEx roadmaps with company objectives.
Own the engineering intelligence dashboards and leadership reports to surface the impact of AI tooling on developer productivity metrics.