Staff Platform Architect, Data & AI

Experian

Staff Platform Architect for Data & AI at Experian, evolving analytics platforms and MLOps infrastructure. Joining a team to enhance technology strategy across enterprise analytics products.

Posted 7/25/2026full-timeRemote • 🇺🇸 United StatesLead💰 $133,109 - $239,596 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in building and operating MLOps platforms, with a strong focus on data governance, analytics infrastructure, and AI/ML systems. Proficient in designing federated catalog architectures and ensuring compliance with security and governance standards in regulated data domains.

Highest-signal resume keywords

MLOps Platform DevelopmentData GovernanceAI Agent-Based ArchitecturesDistributed ComputingCloud-Native Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingFeature EngineeringModel DeploymentMonitoringInfrastructure As CodeSemantic DiscoveryData LineageAPIs DevelopmentAnalytics InfrastructureEnterprise-Scale Data Platforms

Soft Skills

MentoringArchitectural Decision-MakingFirst-Principles Thinking

Tools & Technologies

DatabricksAWSBI ToolsAI AgentsClient-Facing Products

Certifications & Qualifications

Bachelor's Degree In ScienceTechnologyEngineering

Industry Keywords

Credit RiskFinancial ServicesRegulated Data DomainsComplianceGovernance

Tech Stack

Tools & technologies

AWSCloud

About the role

Key responsibilities & impact

Evolve our existing batch, analytics and MLOps platforms improving reliability, cost, and operational efficiency.
Develop the infrastructure for our semantic and ontology layers. (including authoring and governance tooling, lifecycle management, and catalog integration)
Design the usage infrastructure that makes these layers usable by any downstream consumer, including BI tools, ML pipelines, AI agents, internal users and client-facing products
Design agent-driven data access patterns, including permission-aware semantic discovery, identity federation for AI workloads, and APIs that expose platform capabilities to LLM-based agents.
Ensure shared platform capabilities translate cleanly into client-facing products.
Guide technology adoption across engineering teams by making the right architectural choices well-reasoned and easy to follow.
Lead focused prototyping and R&D efforts with analytics product and engineering teams to validate new AI and analytics capabilities before broader platform investment.
Mentor engineers across the organization in your areas of expertise, with a focus on first-principles thinking, system design, and product awareness.

Requirements

What you’ll need

10+ years of software engineering experience, with a deep focus on data platforms, analytics infrastructure, and AI/ML systems at enterprise scale.
Bachelor's Degree or higher in science, technology, engineering or related field
Experience building or operating MLOps platforms from data access and feature engineering through model deployment and monitoring.
Experience with data modeling, metadata, lineage, and data governance
Hands-on experience with AI agent-based architectures, in the context of governed data access, semantic discovery, and retrieval over enterprise data assets.
Experience with distributed computing, cloud-native infrastructure, and the cost and operational dynamics of running large-scale data workloads on public cloud (AWS preferred).
Comfort with infrastructure as code and operating production workloads
Experience influencing architectural decisions at scale, across teams and departments
Experience building enterprise-scale data and MLOps platforms on Databricks
Experience designing federated catalog architectures that deliver governed, unified data access across existing platforms and data silos.
Experience with security, compliance and governance considerations for AI/ML workloads, including data residency, access control and audit requirements.
Background in credit risk, financial services, or other regulated data domains where governance and compliance constraints shape platform design.

Benefits

Comp & perks

Great compensation package and bonus plan
Core benefits including medical, dental, vision, and matching 401K
Flexible work environment, ability to work remote, hybrid or in-office
Flexible time off including volunteer time off, vacation, sick and 12-paid holidays