Staff Platform Architect, Data & AI

Accountabilities:
The Staff Platform Architect, Data & AI will lead the evolution of enterprise data and AI platform architecture, ensuring reliability, scalability, governance, and usability across the organization. This role combines strategic technical leadership with hands-on design, prototyping, and collaboration to accelerate innovation. Evolve existing batch processing, analytics, and MLOps platforms to improve reliability, scalability, cost efficiency, and operational performance. Design and develop AI-ready data foundations, including semantic and ontology layers, governance tooling, lifecycle management, and catalog integration. Build usage infrastructure such as APIs, libraries, and services that enable secure access to data capabilities for internal teams, external products, and AI agents. Develop permission-aware semantic discovery solutions that improve how users and intelligent systems interact with enterprise data assets. Guide engineering teams in adopting effective architectural patterns through clear recommendations, reference designs, and technical collaboration. Lead research, experimentation, and prototyping initiatives with product and engineering teams to validate emerging AI and analytics capabilities. Define and promote best practices for platform architecture, system design, and AI/ML infrastructure development. Mentor engineers across the organization by sharing expertise in architecture, first-principles thinking, and scalable platform development. Influence technical decisions across teams by building alignment through collaboration and demonstrated expertise. Support the ongoing evolution of shared platforms while balancing innovation, security, governance, and operational requirements. Requirements: The ideal candidate will bring extensive experience designing and operating enterprise-scale data platforms, AI/ML systems, and cloud-native infrastructure. Success in this role requires strong architectural judgment, technical depth, and the ability to influence teams through expertise rather than formal authority. 10+ years of software engineering experience focused on data platforms, analytics infrastructure, and AI/ML systems at enterprise scale. Bachelor’s degree or higher in computer science, engineering, technology, or a related technical field. Proven experience building and operating MLOps platforms, including data access, feature engineering, model deployment, and monitoring. Strong understanding of data modeling, schema design, data quality, and platform engineering principles. Experience designing AI agent-based architectures for governed data access, semantic discovery, and enterprise data retrieval. Experience creating federated catalog architectures that enable unified access across multiple platforms and data sources. Knowledge of security, compliance, governance, data residency, and access control considerations for AI/ML workloads. Experience with distributed computing, cloud-native infrastructure, and large-scale data workloads on public cloud platforms. Hands-on experience with infrastructure as code and managing production-grade systems. Demonstrated ability to evolve shared platform architectures over time. Strong communication and collaboration skills with the ability to drive architectural alignment across diverse engineering teams. Experience in regulated industries such as financial services, healthcare, or other data-intensive environments is a plus. Benefits: Competitive compensation package with bonus eligibility. Comprehensive benefits package including medical, dental, and vision coverage. Employer-supported retirement plan with 401(k) matching. Flexible work environment with remote, hybrid, or office-based options. Flexible paid time off, including vacation, sick leave, volunteer time off, and company holidays. Opportunities to work on advanced AI, data, and technology initiatives with global impact. Inclusive workplace culture focused on innovation, collaboration, and professional growth.

How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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