Staff Platform Architect, Data & AI
New
J
JobgetherData & AI
Based in the United StatesFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- Data modelingMLOps
Requirements
- 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.
Responsibilities
- 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.
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