Principal Software Engineer

Principal Engineer, AI/ML & Cloud

Overview

Principal Engineer, AI/ML & Cloud, to serve as the top individual-contributor technical authority for our customer-facing AI capabilities and the cloud platform that powers them. This is a strategy-and-architecture role: you will define the reference architecture for Generative and Agentic AI on the platform, establish the engineering standards other teams build against, and personally deliver the hardest, highest-leverage pieces of the system. You will operate as a force multiplier across the AI/data science and platform engineering teams — shaping direction through architecture, design reviews, and hands-on mentorship rather than through people management.

The ideal candidate brings 13+ years of experience in Data Engineering and ML (including large-scale distributed systems and architecture), 3+ years of hands-on experience with Generative/Agentic AI in production, and a track record of designing and deploying production-grade AI solutions across a hybrid estate — on-prem (Oracle) and AWS Cloud. You set technical vision, make the build/buy and platform decisions that others depend on, and are trusted to be right on the questions that matter most.

Responsibilities

The position’s purpose includes, but is not limited to:

• Own the end-to-end technical architecture for the AI platform — from data foundations through model serving, retrieval, and agent orchestration — and drive it from vision to production.
• Define reference architectures, engineering standards, and paved-path patterns that AI and platform teams build against.
• Make and document the high-stakes build/buy, model-selection, and platform decisions (frameworks, vector stores, inference strategy, cloud services), informing senior leadership of alternatives and trade-offs.
• Lead the analysis and design of net-new and enhanced AI capabilities for the marketing platform as per business strategy and requirements.
• Set the standard for the development environment, tooling, and AI-assisted engineering workflow across teams.
• Partner with QA, product, data science and production support to bring new or enhanced AI features to market and resolve the most complex issues.
• Establish monitoring, evaluation, cost-governance and reliability practices for production AI systems.
• Guide and de-risk platform, security, and version/patch upgrade strategy for the AI and cloud estate.

Duties and responsibilities:

• Act as the principal technical authority on AI and cloud architecture, driving alignment across developers, project managers, business analysts, data scientists and business users on conceptualizing, estimating and delivering new applications and enhancements.
• Own the definition of technical strategy and multi-quarter roadmaps for the AI platform, including scoping, sequencing, and realistic estimates for complex programs of work.
• Lead the definition, development, and documentation of the platform’s technical objectives, architecture, and specifications in collaboration with internal users and departments.
• Author reference architectures and technical design documentation that define system components, the development environment, and implementation strategy for teams to execute against.
• Provide technical direction, review designs and code, and raise the engineering bar for Principal, Staff and Senior engineers — acting as a force multiplier rather than a people manager.
• Partner with the QA organization to define test strategy, quality metrics, and evaluation frameworks for AI systems, and to resolve questions about results.
• Recommend and drive architecture and business-process improvements, and clearly communicate problem/resolution processes to leadership.
• Develop and implement solutions in accordance with policies, procedures, and security/compliance requirements, and shape those policies where they need to evolve.
• Facilitate the design and implementation of new products and platform initiatives, informing senior executives of technical alternatives, risks, and trade-offs.
• Set the standard for project and architecture documentation across the practice.
• Lead root cause analysis (RCA) on the most complex, cross-cutting incidents.
• Work with cross-functional teams during critical incidents to assess, analyze, and resolve complex problems that span multiple systems and teams.
• Identify where existing engineering policies, standards and procedures require improvement and drive t