AI/Data Architect

The AI/Data Architect provides strategic oversight and technical leadership across Product Management, Data Engineering, and Data Science teams. This role defines the enterprise AI and data architecture vision, establishes standards, ensures scalable and secure solutions, and guides delivery teams from concept through production deployment., Own enterprise AI and data architecture strategy, roadmap, and governance.

Define target-state architecture for data platforms, analytics, machine learning, GenAI, and agentic AI solutions.

Provide architectural guidance and design reviews for Product Managers, Data Engineers, Data Scientists, and delivery teams.

Establish best practices for data modeling, integration, MLOps, DataOps, AI governance, observability, and security.

Ensure alignment between business objectives, product roadmaps, and technology investments.

Lead architecture reviews, technology selection, reference architectures, and solution blueprints.

Drive adoption of cloud-native data and AI platforms across Azure, AWS, or Google Cloud Platform.

Provide oversight on scalability, reliability, performance, compliance, and cost optimization.

Mentor technical teams and foster architecture excellence across the organization.

Serve as a trusted advisor for executive stakeholders and customers.

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.

15+ years of experience in data, analytics, AI, or enterprise architecture roles.

Experience leading large-scale cloud data platform and AI transformation programs.

Strong expertise in enterprise data architecture, distributed systems, and modern data platforms.

Hands-on knowledge of machine learning, GenAI, LLMs, vector databases, RAG architectures, and AI agents.

Experience with Azure Data Platform, Databricks, Snowflake, Fabric, or equivalent technologies.

Knowledge of data governance, privacy, security, and responsible AI frameworks.

Excellent stakeholder management and executive communication skills.

Preferred Skills

Architecture certifications in Azure, AWS, Google Cloud Platform, TOGAF, or equivalent.

Experience in regulated industries and enterprise-scale delivery environments.

Experience building AI Centers of Excellence and architecture governance programs.

Success Measures

Adoption of architecture standards and reusable patterns.

Successful delivery of scalable, secure, and cost-effective AI/data solutions.

Reduction in technical debt and architecture risks.

Improved platform reliability, AI model lifecycle maturity, and business value realization.