Principal Software Engineer, AI (Web & Data)

Requirements Experience: 8–10 years in software engineering, specializing in SaaS platform architecture and distributed systems design

Leadership & Advocacy: Excellence in technical diplomacy and stakeholder influence. Proven ability to translate complex AI/Data roadmaps into business value while mentoring senior teams and advocating for architectural best practices

Data Orchestration: Strong hands‑on experience with Apache Airflow (or similar), Kafka, and dbt to support real‑time AI applications

AI & Web Convergence: Proven expertise integrating LLMs into production web environments with a focus on agentic workflows and autonomous UI generation

Architectural Vision: Deep understanding of headless CMS, composable, and event‑driven patterns that allow for programmatic content and UI generation

Modern Delivery: Experience with cloud‑native technologies (AWS/GCP, Docker, Kubernetes) and a deep expertise in data privacy frameworks and AI ethics

What the job involves We are seeking an accomplished Principal Engineer to lead the technical architecture and evolution of our hybrid digital ecosystem

In this pivotal role, you will drive the strategic integration of multi‑modal AI into our core infrastructure while architecting an omnichannel platform capable of supporting complex customer journeys

You will balance the adoption of proven third‑party AI solutions (OpenAI, Anthropic) with the development of proprietary optimizations where they deliver a competitive advantage

As a technical leader, you will champion a vendor‑agnostic, ethically grounded, and Privacy by Design approach to AI implementation

Infrastructure Modernization: Lead the roadmap for migrating legacy data pipelines to AI‑native architectures. You will design modern data orchestration solutions utilizing Apache Airflow, dbt, and Kafka to replace outdated batch processing with real‑time, event‑driven flows

AI‑Native Web Transformation: Define the web infrastructure required for a high‑agility ecosystem. Architect the evolution of our Headless CMS environments (AEM, Contentful) and modern frontend frameworks to enable automated page assembly and AI‑driven UI components

Agentic Pipeline Development: Design and build autonomous pipelines that bridge the gap between design systems, automated component development, and publishing workflows

ML & Data Engineering Leadership: Establish standards for RAG, vector databases, and LLM orchestration. Provide architectural guidance for the seamless integration of AI capabilities across headless and omnichannel systems while ensuring consistency, performance, and security

Engineering Productivity: Champion the use of AI‑assisted coding and engineering productivity tools such as Claude Code and Cursor to accelerate development cycles and optimize architectural decision‑making

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